Overview

Dataset statistics

Number of variables28
Number of observations522517
Missing cells1132236
Missing cells (%)7.7%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory111.6 MiB
Average record size in memory224.0 B

Variable types

Numeric14
Text13
DateTime1

Alerts

AuthorId is highly overall correlated with RecipeIdHigh correlation
Calories is highly overall correlated with CarbohydrateContent and 5 other fieldsHigh correlation
CarbohydrateContent is highly overall correlated with Calories and 2 other fieldsHigh correlation
CholesterolContent is highly overall correlated with Calories and 3 other fieldsHigh correlation
FatContent is highly overall correlated with Calories and 4 other fieldsHigh correlation
FiberContent is highly overall correlated with CarbohydrateContentHigh correlation
ProteinContent is highly overall correlated with Calories and 4 other fieldsHigh correlation
RecipeId is highly overall correlated with AuthorIdHigh correlation
SaturatedFatContent is highly overall correlated with Calories and 3 other fieldsHigh correlation
SodiumContent is highly overall correlated with Calories and 2 other fieldsHigh correlation
SugarContent is highly overall correlated with CarbohydrateContentHigh correlation
CookTime has 82545 (15.8%) missing valuesMissing
Keywords has 17237 (3.3%) missing valuesMissing
AggregatedRating has 253223 (48.5%) missing valuesMissing
ReviewCount has 247489 (47.4%) missing valuesMissing
RecipeServings has 182911 (35.0%) missing valuesMissing
RecipeYield has 348071 (66.6%) missing valuesMissing
ReviewCount is highly skewed (γ1 = 42.51088124)Skewed
Calories is highly skewed (γ1 = 252.2144508)Skewed
FatContent is highly skewed (γ1 = 410.5726004)Skewed
SaturatedFatContent is highly skewed (γ1 = 409.7743347)Skewed
CholesterolContent is highly skewed (γ1 = 250.1401405)Skewed
SodiumContent is highly skewed (γ1 = 102.2432655)Skewed
CarbohydrateContent is highly skewed (γ1 = 413.8270231)Skewed
FiberContent is highly skewed (γ1 = 118.7003438)Skewed
SugarContent is highly skewed (γ1 = 493.4705449)Skewed
ProteinContent is highly skewed (γ1 = 208.6479159)Skewed
RecipeServings is highly skewed (γ1 = 277.9793288)Skewed
RecipeId has unique valuesUnique
FatContent has 11340 (2.2%) zerosZeros
SaturatedFatContent has 27584 (5.3%) zerosZeros
CholesterolContent has 110399 (21.1%) zerosZeros
CarbohydrateContent has 5693 (1.1%) zerosZeros
FiberContent has 27001 (5.2%) zerosZeros
SugarContent has 11802 (2.3%) zerosZeros
ProteinContent has 8403 (1.6%) zerosZeros

Reproduction

Analysis started2024-01-05 15:19:37.270669
Analysis finished2024-01-05 15:21:49.731052
Duration2 minutes and 12.46 seconds
Software versionydata-profiling vv4.6.2
Download configurationconfig.json

Variables

RecipeId
Real number (ℝ)

HIGH CORRELATION  UNIQUE 

Distinct522517
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean271821.44
Minimum38
Maximum541383
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size4.0 MiB
2024-01-05T16:21:49.765510image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

Quantile statistics

Minimum38
5-th percentile29659.8
Q1137206
median271758
Q3406145
95-th percentile514600.2
Maximum541383
Range541345
Interquartile range (IQR)268939

Descriptive statistics

Standard deviation155495.88
Coefficient of variation (CV)0.57205157
Kurtosis-1.197551
Mean271821.44
Median Absolute Deviation (MAD)134472
Skewness0.0019598952
Sum1.4203132 × 1011
Variance2.4178968 × 1010
MonotonicityStrictly increasing
2024-01-05T16:21:49.810702image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
38 1
 
< 0.1%
361220 1
 
< 0.1%
361234 1
 
< 0.1%
361233 1
 
< 0.1%
361232 1
 
< 0.1%
361231 1
 
< 0.1%
361230 1
 
< 0.1%
361229 1
 
< 0.1%
361228 1
 
< 0.1%
361227 1
 
< 0.1%
Other values (522507) 522507
> 99.9%
ValueCountFrequency (%)
38 1
< 0.1%
39 1
< 0.1%
40 1
< 0.1%
41 1
< 0.1%
42 1
< 0.1%
43 1
< 0.1%
44 1
< 0.1%
45 1
< 0.1%
46 1
< 0.1%
47 1
< 0.1%
ValueCountFrequency (%)
541383 1
< 0.1%
541382 1
< 0.1%
541381 1
< 0.1%
541380 1
< 0.1%
541379 1
< 0.1%
541378 1
< 0.1%
541377 1
< 0.1%
541376 1
< 0.1%
541375 1
< 0.1%
541374 1
< 0.1%

Name
Text

Distinct438188
Distinct (%)83.9%
Missing0
Missing (%)0.0%
Memory size4.0 MiB
2024-01-05T16:21:50.047861image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

Length

Max length97
Median length74
Mean length26.649753
Min length2

Characters and Unicode

Total characters13924949
Distinct characters140
Distinct categories18 ?
Distinct scripts2 ?
Distinct blocks5 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique405588 ?
Unique (%)77.6%

Sample

1st rowLow-Fat Berry Blue Frozen Dessert
2nd rowBiryani
3rd rowBest Lemonade
4th rowCarina's Tofu-Vegetable Kebabs
5th rowCabbage Soup
ValueCountFrequency (%)
and 61825
 
3.0%
with 54457
 
2.6%
chicken 49017
 
2.3%
salad 30004
 
1.4%
sauce 21485
 
1.0%
soup 21384
 
1.0%
chocolate 20099
 
1.0%
cake 19528
 
0.9%
cheese 19214
 
0.9%
16057
 
0.8%
Other values (68881) 1777023
85.0%
2024-01-05T16:21:50.310817image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
1575656
 
11.3%
e 1345613
 
9.7%
a 1190728
 
8.6%
i 760954
 
5.5%
o 739114
 
5.3%
r 709199
 
5.1%
n 657193
 
4.7%
t 621701
 
4.5%
s 580715
 
4.2%
l 490599
 
3.5%
Other values (130) 5253477
37.7%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 10059964
72.2%
Uppercase Letter 2018719
 
14.5%
Space Separator 1575656
 
11.3%
Other Punctuation 131107
 
0.9%
Dash Punctuation 60722
 
0.4%
Open Punctuation 32123
 
0.2%
Close Punctuation 31713
 
0.2%
Decimal Number 13638
 
0.1%
Final Punctuation 735
 
< 0.1%
Math Symbol 350
 
< 0.1%
Other values (8) 222
 
< 0.1%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
e 1345613
13.4%
a 1190728
11.8%
i 760954
 
7.6%
o 739114
 
7.3%
r 709199
 
7.0%
n 657193
 
6.5%
t 621701
 
6.2%
s 580715
 
5.8%
l 490599
 
4.9%
h 386999
 
3.8%
Other values (33) 2577149
25.6%
Uppercase Letter
ValueCountFrequency (%)
C 360959
17.9%
S 297050
14.7%
B 201824
10.0%
P 185243
9.2%
M 110469
 
5.5%
W 90230
 
4.5%
T 87676
 
4.3%
F 80635
 
4.0%
R 79477
 
3.9%
G 70704
 
3.5%
Other values (19) 454452
22.5%
Other Punctuation
ValueCountFrequency (%)
' 43295
33.0%
& 24320
18.5%
; 22317
17.0%
, 18670
14.2%
! 6408
 
4.9%
. 5811
 
4.4%
/ 5549
 
4.2%
# 2351
 
1.8%
: 954
 
0.7%
" 754
 
0.6%
Other values (10) 678
 
0.5%
Decimal Number
ValueCountFrequency (%)
1 2599
19.1%
5 2393
17.5%
2 2238
16.4%
0 1646
12.1%
3 1432
10.5%
4 983
 
7.2%
9 688
 
5.0%
7 609
 
4.5%
6 584
 
4.3%
8 466
 
3.4%
Open Punctuation
ValueCountFrequency (%)
( 31983
99.6%
[ 111
 
0.3%
{ 27
 
0.1%
1
 
< 0.1%
1
 
< 0.1%
Modifier Symbol
ValueCountFrequency (%)
^ 4
44.4%
´ 2
22.2%
¨ 1
 
11.1%
¸ 1
 
11.1%
˜ 1
 
11.1%
Math Symbol
ValueCountFrequency (%)
= 214
61.1%
+ 106
30.3%
| 28
 
8.0%
± 2
 
0.6%
Currency Symbol
ValueCountFrequency (%)
$ 58
95.1%
£ 1
 
1.6%
¥ 1
 
1.6%
¢ 1
 
1.6%
Dash Punctuation
ValueCountFrequency (%)
- 60628
99.8%
85
 
0.1%
9
 
< 0.1%
Close Punctuation
ValueCountFrequency (%)
) 31575
99.6%
] 113
 
0.4%
} 25
 
0.1%
Other Symbol
ValueCountFrequency (%)
19
86.4%
® 2
 
9.1%
° 1
 
4.5%
Final Punctuation
ValueCountFrequency (%)
668
90.9%
67
 
9.1%
Initial Punctuation
ValueCountFrequency (%)
65
73.9%
23
 
26.1%
Other Number
ValueCountFrequency (%)
½ 7
77.8%
¹ 2
 
22.2%
Other Letter
ValueCountFrequency (%)
º 1
50.0%
ª 1
50.0%
Space Separator
ValueCountFrequency (%)
1575656
100.0%
Connector Punctuation
ValueCountFrequency (%)
_ 20
100.0%
Control
ValueCountFrequency (%)
11
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 12078685
86.7%
Common 1846264
 
13.3%

Most frequent character per script

Latin
ValueCountFrequency (%)
e 1345613
 
11.1%
a 1190728
 
9.9%
i 760954
 
6.3%
o 739114
 
6.1%
r 709199
 
5.9%
n 657193
 
5.4%
t 621701
 
5.1%
s 580715
 
4.8%
l 490599
 
4.1%
h 386999
 
3.2%
Other values (64) 4595870
38.0%
Common
ValueCountFrequency (%)
1575656
85.3%
- 60628
 
3.3%
' 43295
 
2.3%
( 31983
 
1.7%
) 31575
 
1.7%
& 24320
 
1.3%
; 22317
 
1.2%
, 18670
 
1.0%
! 6408
 
0.3%
. 5811
 
0.3%
Other values (56) 25601
 
1.4%

Most occurring blocks

ValueCountFrequency (%)
ASCII 13923928
> 99.9%
Punctuation 928
 
< 0.1%
None 73
 
< 0.1%
Letterlike Symbols 19
 
< 0.1%
Modifier Letters 1
 
< 0.1%

Most frequent character per block

ASCII
ValueCountFrequency (%)
1575656
 
11.3%
e 1345613
 
9.7%
a 1190728
 
8.6%
i 760954
 
5.5%
o 739114
 
5.3%
r 709199
 
5.1%
n 657193
 
4.7%
t 621701
 
4.5%
s 580715
 
4.2%
l 490599
 
3.5%
Other values (81) 5252456
37.7%
Punctuation
ValueCountFrequency (%)
668
72.0%
85
 
9.2%
67
 
7.2%
65
 
7.0%
23
 
2.5%
9
 
1.0%
5
 
0.5%
2
 
0.2%
1
 
0.1%
1
 
0.1%
Other values (2) 2
 
0.2%
Letterlike Symbols
ValueCountFrequency (%)
19
100.0%
None
ValueCountFrequency (%)
é 9
 
12.3%
½ 7
 
9.6%
è 5
 
6.8%
ï 5
 
6.8%
¿ 5
 
6.8%
ž 4
 
5.5%
ñ 3
 
4.1%
å 3
 
4.1%
¹ 2
 
2.7%
ä 2
 
2.7%
Other values (25) 28
38.4%
Modifier Letters
ValueCountFrequency (%)
˜ 1
100.0%

AuthorId
Real number (ℝ)

HIGH CORRELATION 

Distinct57178
Distinct (%)10.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean45725848
Minimum27
Maximum2.0028861 × 109
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size4.0 MiB
2024-01-05T16:21:50.383367image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

Quantile statistics

Minimum27
5-th percentile15729
Q169474
median238937
Q3565828
95-th percentile1779984
Maximum2.0028861 × 109
Range2.0028861 × 109
Interquartile range (IQR)496354

Descriptive statistics

Standard deviation2.9297145 × 108
Coefficient of variation (CV)6.4071299
Kurtosis38.130657
Mean45725848
Median Absolute Deviation (MAD)188806
Skewness6.3272549
Sum2.3892533 × 1013
Variance8.583227 × 1016
MonotonicityNot monotonic
2024-01-05T16:21:50.427659image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
37779 7742
 
1.5%
283251 6375
 
1.2%
89831 3926
 
0.8%
57042 3435
 
0.7%
883095 3399
 
0.7%
37449 3301
 
0.6%
1533 3032
 
0.6%
1072593 2841
 
0.5%
287420 2260
 
0.4%
6357 2088
 
0.4%
Other values (57168) 484118
92.7%
ValueCountFrequency (%)
27 8
 
< 0.1%
1530 62
 
< 0.1%
1531 10
 
< 0.1%
1532 1
 
< 0.1%
1533 3032
0.6%
1534 599
 
0.1%
1535 444
 
0.1%
1536 1
 
< 0.1%
1537 3
 
< 0.1%
1538 59
 
< 0.1%
ValueCountFrequency (%)
2002886148 1
< 0.1%
2002884746 1
< 0.1%
2002882138 1
< 0.1%
2002872638 1
< 0.1%
2002869562 1
< 0.1%
2002869245 1
< 0.1%
2002865126 2
< 0.1%
2002861170 2
< 0.1%
2002861027 1
< 0.1%
2002859792 2
< 0.1%
Distinct56793
Distinct (%)10.9%
Missing0
Missing (%)0.0%
Memory size4.0 MiB
2024-01-05T16:21:50.625183image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

Length

Max length20
Median length16
Mean length10.319044
Min length3

Characters and Unicode

Total characters5391876
Distinct characters66
Distinct categories7 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique31069 ?
Unique (%)5.9%

Sample

1st rowDancer
2nd rowelly9812
3rd rowStephen Little
4th rowCyclopz
5th rowDuckie067
ValueCountFrequency (%)
chef 16451
 
2.2%
in 13561
 
1.8%
ratherbeswimmin 7742
 
1.1%
dicentra 6375
 
0.9%
kittencalrecipezazz 3926
 
0.5%
the 3895
 
0.5%
internetnut 3435
 
0.5%
j 3400
 
0.5%
mariajane 3399
 
0.5%
sharon123 3301
 
0.4%
Other values (51770) 670696
91.1%
2024-01-05T16:21:50.893716image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
e 534625
 
9.9%
a 444992
 
8.3%
i 355339
 
6.6%
n 339944
 
6.3%
r 293800
 
5.4%
o 264682
 
4.9%
l 229115
 
4.2%
214733
 
4.0%
t 214177
 
4.0%
s 199890
 
3.7%
Other values (56) 2300579
42.7%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 4131274
76.6%
Uppercase Letter 738646
 
13.7%
Decimal Number 253064
 
4.7%
Space Separator 214733
 
4.0%
Other Punctuation 24688
 
0.5%
Connector Punctuation 19386
 
0.4%
Dash Punctuation 10085
 
0.2%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
e 534625
12.9%
a 444992
10.8%
i 355339
 
8.6%
n 339944
 
8.2%
r 293800
 
7.1%
o 264682
 
6.4%
l 229115
 
5.5%
t 214177
 
5.2%
s 199890
 
4.8%
h 157483
 
3.8%
Other values (16) 1097227
26.6%
Uppercase Letter
ValueCountFrequency (%)
C 77299
 
10.5%
M 69125
 
9.4%
S 55942
 
7.6%
B 47947
 
6.5%
A 41506
 
5.6%
L 40331
 
5.5%
D 38297
 
5.2%
K 38092
 
5.2%
J 36316
 
4.9%
R 34750
 
4.7%
Other values (16) 259041
35.1%
Decimal Number
ValueCountFrequency (%)
2 41532
16.4%
1 35660
14.1%
3 28073
11.1%
0 24730
9.8%
5 23217
9.2%
6 22661
9.0%
4 21240
8.4%
8 20701
8.2%
7 18919
7.5%
9 16331
 
6.5%
Space Separator
ValueCountFrequency (%)
214733
100.0%
Other Punctuation
ValueCountFrequency (%)
. 24688
100.0%
Connector Punctuation
ValueCountFrequency (%)
_ 19386
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 10085
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 4869920
90.3%
Common 521956
 
9.7%

Most frequent character per script

Latin
ValueCountFrequency (%)
e 534625
 
11.0%
a 444992
 
9.1%
i 355339
 
7.3%
n 339944
 
7.0%
r 293800
 
6.0%
o 264682
 
5.4%
l 229115
 
4.7%
t 214177
 
4.4%
s 199890
 
4.1%
h 157483
 
3.2%
Other values (42) 1835873
37.7%
Common
ValueCountFrequency (%)
214733
41.1%
2 41532
 
8.0%
1 35660
 
6.8%
3 28073
 
5.4%
0 24730
 
4.7%
. 24688
 
4.7%
5 23217
 
4.4%
6 22661
 
4.3%
4 21240
 
4.1%
8 20701
 
4.0%
Other values (4) 64721
 
12.4%

Most occurring blocks

ValueCountFrequency (%)
ASCII 5391876
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
e 534625
 
9.9%
a 444992
 
8.3%
i 355339
 
6.6%
n 339944
 
6.3%
r 293800
 
5.4%
o 264682
 
4.9%
l 229115
 
4.2%
214733
 
4.0%
t 214177
 
4.0%
s 199890
 
3.7%
Other values (56) 2300579
42.7%

CookTime
Text

MISSING 

Distinct490
Distinct (%)0.1%
Missing82545
Missing (%)15.8%
Memory size4.0 MiB
2024-01-05T16:21:51.122852image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

Length

Max length9
Median length5
Mean length4.8512019
Min length4

Characters and Unicode

Total characters2134393
Distinct characters14
Distinct categories2 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique179 ?
Unique (%)< 0.1%

Sample

1st rowPT24H
2nd rowPT25M
3rd rowPT5M
4th rowPT20M
5th rowPT30M
ValueCountFrequency (%)
pt30m 50715
11.5%
pt20m 47998
10.9%
pt15m 44769
 
10.2%
pt10m 42431
 
9.6%
pt1h 34634
 
7.9%
pt25m 24609
 
5.6%
pt45m 24046
 
5.5%
pt40m 19247
 
4.4%
pt5m 18979
 
4.3%
pt35m 14519
 
3.3%
Other values (480) 118025
26.8%
2024-01-05T16:21:51.369438image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
P 439972
20.6%
T 439972
20.6%
M 365250
17.1%
0 187081
8.8%
1 168370
 
7.9%
5 154068
 
7.2%
2 106565
 
5.0%
H 98352
 
4.6%
3 89957
 
4.2%
4 56342
 
2.6%
Other values (4) 28464
 
1.3%

Most occurring categories

ValueCountFrequency (%)
Uppercase Letter 1343546
62.9%
Decimal Number 790847
37.1%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 187081
23.7%
1 168370
21.3%
5 154068
19.5%
2 106565
13.5%
3 89957
11.4%
4 56342
 
7.1%
8 12968
 
1.6%
6 8497
 
1.1%
7 5016
 
0.6%
9 1983
 
0.3%
Uppercase Letter
ValueCountFrequency (%)
P 439972
32.7%
T 439972
32.7%
M 365250
27.2%
H 98352
 
7.3%

Most occurring scripts

ValueCountFrequency (%)
Latin 1343546
62.9%
Common 790847
37.1%

Most frequent character per script

Common
ValueCountFrequency (%)
0 187081
23.7%
1 168370
21.3%
5 154068
19.5%
2 106565
13.5%
3 89957
11.4%
4 56342
 
7.1%
8 12968
 
1.6%
6 8497
 
1.1%
7 5016
 
0.6%
9 1983
 
0.3%
Latin
ValueCountFrequency (%)
P 439972
32.7%
T 439972
32.7%
M 365250
27.2%
H 98352
 
7.3%

Most occurring blocks

ValueCountFrequency (%)
ASCII 2134393
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
P 439972
20.6%
T 439972
20.6%
M 365250
17.1%
0 187081
8.8%
1 168370
 
7.9%
5 154068
 
7.2%
2 106565
 
5.0%
H 98352
 
4.6%
3 89957
 
4.2%
4 56342
 
2.6%
Other values (4) 28464
 
1.3%
Distinct318
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size4.0 MiB
2024-01-05T16:21:51.480295image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

Length

Max length9
Median length5
Mean length4.7781929
Min length4

Characters and Unicode

Total characters2496687
Distinct characters16
Distinct categories3 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique117 ?
Unique (%)< 0.1%

Sample

1st rowPT45M
2nd rowPT4H
3rd rowPT30M
4th rowPT24H
5th rowPT20M
ValueCountFrequency (%)
pt10m 120265
23.0%
pt15m 107428
20.6%
pt5m 74490
14.3%
pt20m 72260
13.8%
pt30m 47144
 
9.0%
pt0s 15010
 
2.9%
pt25m 14892
 
2.9%
pt1h 10497
 
2.0%
pt45m 8990
 
1.7%
pt2m 7489
 
1.4%
Other values (308) 44052
 
8.4%
2024-01-05T16:21:51.617843image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
P 522517
20.9%
T 522517
20.9%
M 484223
19.4%
0 266310
10.7%
1 250882
10.0%
5 213434
8.5%
2 104652
 
4.2%
3 60587
 
2.4%
H 29488
 
1.2%
4 19896
 
0.8%
Other values (6) 22181
 
0.9%

Most occurring categories

ValueCountFrequency (%)
Uppercase Letter 1573755
63.0%
Decimal Number 922930
37.0%
Dash Punctuation 2
 
< 0.1%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 266310
28.9%
1 250882
27.2%
5 213434
23.1%
2 104652
 
11.3%
3 60587
 
6.6%
4 19896
 
2.2%
8 3372
 
0.4%
7 1801
 
0.2%
6 1587
 
0.2%
9 409
 
< 0.1%
Uppercase Letter
ValueCountFrequency (%)
P 522517
33.2%
T 522517
33.2%
M 484223
30.8%
H 29488
 
1.9%
S 15010
 
1.0%
Dash Punctuation
ValueCountFrequency (%)
- 2
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 1573755
63.0%
Common 922932
37.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 266310
28.9%
1 250882
27.2%
5 213434
23.1%
2 104652
 
11.3%
3 60587
 
6.6%
4 19896
 
2.2%
8 3372
 
0.4%
7 1801
 
0.2%
6 1587
 
0.2%
9 409
 
< 0.1%
Latin
ValueCountFrequency (%)
P 522517
33.2%
T 522517
33.2%
M 484223
30.8%
H 29488
 
1.9%
S 15010
 
1.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 2496687
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
P 522517
20.9%
T 522517
20.9%
M 484223
19.4%
0 266310
10.7%
1 250882
10.0%
5 213434
8.5%
2 104652
 
4.2%
3 60587
 
2.4%
H 29488
 
1.2%
4 19896
 
0.8%
Other values (6) 22181
 
0.9%
Distinct1240
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Memory size4.0 MiB
2024-01-05T16:21:51.807525image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

Length

Max length11
Median length5
Mean length5.3577606
Min length4

Characters and Unicode

Total characters2799521
Distinct characters16
Distinct categories3 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique404 ?
Unique (%)0.1%

Sample

1st rowPT24H45M
2nd rowPT4H25M
3rd rowPT35M
4th rowPT24H20M
5th rowPT50M
ValueCountFrequency (%)
pt30m 41590
 
8.0%
pt20m 32908
 
6.3%
pt40m 31349
 
6.0%
pt25m 28420
 
5.4%
pt15m 27856
 
5.3%
pt35m 26858
 
5.1%
pt45m 26370
 
5.0%
pt10m 25592
 
4.9%
pt1h 24658
 
4.7%
pt50m 22786
 
4.4%
Other values (1230) 234130
44.8%
2024-01-05T16:21:52.044299image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
P 522517
18.7%
T 522517
18.7%
M 484643
17.3%
5 255282
9.1%
1 231100
8.3%
0 227177
8.1%
H 170476
 
6.1%
2 139189
 
5.0%
3 118147
 
4.2%
4 87859
 
3.1%
Other values (6) 40614
 
1.5%

Most occurring categories

ValueCountFrequency (%)
Uppercase Letter 1702282
60.8%
Decimal Number 1097238
39.2%
Dash Punctuation 1
 
< 0.1%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
5 255282
23.3%
1 231100
21.1%
0 227177
20.7%
2 139189
12.7%
3 118147
10.8%
4 87859
 
8.0%
8 13578
 
1.2%
7 10906
 
1.0%
6 10339
 
0.9%
9 3661
 
0.3%
Uppercase Letter
ValueCountFrequency (%)
P 522517
30.7%
T 522517
30.7%
M 484643
28.5%
H 170476
 
10.0%
S 2129
 
0.1%
Dash Punctuation
ValueCountFrequency (%)
- 1
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 1702282
60.8%
Common 1097239
39.2%

Most frequent character per script

Common
ValueCountFrequency (%)
5 255282
23.3%
1 231100
21.1%
0 227177
20.7%
2 139189
12.7%
3 118147
10.8%
4 87859
 
8.0%
8 13578
 
1.2%
7 10906
 
1.0%
6 10339
 
0.9%
9 3661
 
0.3%
Latin
ValueCountFrequency (%)
P 522517
30.7%
T 522517
30.7%
M 484643
28.5%
H 170476
 
10.0%
S 2129
 
0.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII 2799521
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
P 522517
18.7%
T 522517
18.7%
M 484643
17.3%
5 255282
9.1%
1 231100
8.3%
0 227177
8.1%
H 170476
 
6.1%
2 139189
 
5.0%
3 118147
 
4.2%
4 87859
 
3.1%
Other values (6) 40614
 
1.5%
Distinct245540
Distinct (%)47.0%
Missing0
Missing (%)0.0%
Memory size4.0 MiB
Minimum1999-08-06 00:40:00+00:00
Maximum2020-12-22 22:12:00+00:00
2024-01-05T16:21:52.119494image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2024-01-05T16:21:52.166072image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
Distinct492838
Distinct (%)94.3%
Missing5
Missing (%)< 0.1%
Memory size4.0 MiB
2024-01-05T16:21:52.748092image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

Length

Max length6325
Median length2224
Mean length186.79932
Min length44

Characters and Unicode

Total characters97604887
Distinct characters153
Distinct categories18 ?
Distinct scripts2 ?
Distinct blocks6 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique479914 ?
Unique (%)91.8%

Sample

1st rowMake and share this Low-Fat Berry Blue Frozen Dessert recipe from Food.com.
2nd rowMake and share this Biryani recipe from Food.com.
3rd rowThis is from one of my first Good House Keeping cookbooks. You must use a *zester* in order to avoid getting any of that bitter rind, and when you zest the lemons, zest them onto some sugar from the recipe (the sugar will 'catch' all of the oils). I also advise you from personal experience to use only the best skinned lemons for the best flavor.
4th rowThis dish is best prepared a day in advance to allow the ingredients to soak in the marinade overnight.
5th rowMake and share this Cabbage Soup recipe from Food.com.
ValueCountFrequency (%)
and 651054
 
3.8%
the 579719
 
3.3%
this 561943
 
3.2%
a 481826
 
2.8%
recipe 405279
 
2.3%
i 364089
 
2.1%
from 329712
 
1.9%
to 329527
 
1.9%
it 291146
 
1.7%
make 284285
 
1.6%
Other values (209378) 13072665
75.3%
2024-01-05T16:21:53.101361image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
17103010
17.5%
e 9191411
 
9.4%
a 6276097
 
6.4%
o 6082761
 
6.2%
t 5903172
 
6.0%
i 5412363
 
5.5%
s 4971246
 
5.1%
r 4714610
 
4.8%
n 4135560
 
4.2%
h 3493882
 
3.6%
Other values (143) 30320775
31.1%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 73258607
75.1%
Space Separator 17103011
 
17.5%
Uppercase Letter 3503342
 
3.6%
Other Punctuation 2890300
 
3.0%
Decimal Number 351910
 
0.4%
Dash Punctuation 190675
 
0.2%
Control 156211
 
0.2%
Close Punctuation 72868
 
0.1%
Open Punctuation 62303
 
0.1%
Final Punctuation 6223
 
< 0.1%
Other values (8) 9437
 
< 0.1%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
e 9191411
12.5%
a 6276097
 
8.6%
o 6082761
 
8.3%
t 5903172
 
8.1%
i 5412363
 
7.4%
s 4971246
 
6.8%
r 4714610
 
6.4%
n 4135560
 
5.6%
h 3493882
 
4.8%
d 2953696
 
4.0%
Other values (32) 20123809
27.5%
Uppercase Letter
ValueCountFrequency (%)
I 554894
15.8%
T 389261
11.1%
M 343965
9.8%
F 312846
 
8.9%
C 269422
 
7.7%
S 247241
 
7.1%
A 163768
 
4.7%
B 153955
 
4.4%
P 138830
 
4.0%
W 101966
 
2.9%
Other values (21) 827194
23.6%
Other Punctuation
ValueCountFrequency (%)
. 1474264
51.0%
, 562416
 
19.5%
' 257487
 
8.9%
! 198596
 
6.9%
; 134800
 
4.7%
& 122145
 
4.2%
/ 51627
 
1.8%
: 43054
 
1.5%
* 12985
 
0.4%
? 11494
 
0.4%
Other values (10) 21432
 
0.7%
Decimal Number
ValueCountFrequency (%)
1 69556
19.8%
0 60602
17.2%
2 60140
17.1%
3 30653
8.7%
4 28220
8.0%
5 27238
 
7.7%
9 23132
 
6.6%
8 19189
 
5.5%
6 18269
 
5.2%
7 14911
 
4.2%
Control
ValueCountFrequency (%)
79644
51.0%
76386
48.9%
176
 
0.1%
1
 
< 0.1%
 1
 
< 0.1%
 1
 
< 0.1%
 1
 
< 0.1%
 1
 
< 0.1%
Math Symbol
ValueCountFrequency (%)
= 2829
68.4%
+ 1054
 
25.5%
| 208
 
5.0%
> 24
 
0.6%
< 13
 
0.3%
~ 4
 
0.1%
± 2
 
< 0.1%
Modifier Symbol
ValueCountFrequency (%)
^ 155
89.6%
` 13
 
7.5%
´ 2
 
1.2%
¨ 2
 
1.2%
¯ 1
 
0.6%
Open Punctuation
ValueCountFrequency (%)
( 61779
99.2%
[ 443
 
0.7%
{ 78
 
0.1%
3
 
< 0.1%
Other Symbol
ValueCountFrequency (%)
38
82.6%
3
 
6.5%
® 3
 
6.5%
° 2
 
4.3%
Dash Punctuation
ValueCountFrequency (%)
- 189468
99.4%
605
 
0.3%
602
 
0.3%
Close Punctuation
ValueCountFrequency (%)
) 72298
99.2%
] 483
 
0.7%
} 87
 
0.1%
Final Punctuation
ValueCountFrequency (%)
5030
80.8%
1191
 
19.1%
» 2
 
< 0.1%
Initial Punctuation
ValueCountFrequency (%)
1176
85.7%
195
 
14.2%
2
 
0.1%
Currency Symbol
ValueCountFrequency (%)
$ 1132
98.1%
21
 
1.8%
¥ 1
 
0.1%
Other Number
ValueCountFrequency (%)
½ 49
92.5%
¹ 3
 
5.7%
¼ 1
 
1.9%
Space Separator
ValueCountFrequency (%)
17103010
> 99.9%
  1
 
< 0.1%
Connector Punctuation
ValueCountFrequency (%)
_ 2503
100.0%
Other Letter
ValueCountFrequency (%)
ª 1
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 76761950
78.6%
Common 20842937
 
21.4%

Most frequent character per script

Common
ValueCountFrequency (%)
17103010
82.1%
. 1474264
 
7.1%
, 562416
 
2.7%
' 257487
 
1.2%
! 198596
 
1.0%
- 189468
 
0.9%
; 134800
 
0.6%
& 122145
 
0.6%
79644
 
0.4%
76386
 
0.4%
Other values (69) 644721
 
3.1%
Latin
ValueCountFrequency (%)
e 9191411
 
12.0%
a 6276097
 
8.2%
o 6082761
 
7.9%
t 5903172
 
7.7%
i 5412363
 
7.1%
s 4971246
 
6.5%
r 4714610
 
6.1%
n 4135560
 
5.4%
h 3493882
 
4.6%
d 2953696
 
3.8%
Other values (64) 23627152
30.8%

Most occurring blocks

ValueCountFrequency (%)
ASCII 97595340
> 99.9%
Punctuation 9281
 
< 0.1%
None 204
 
< 0.1%
Letterlike Symbols 38
 
< 0.1%
Currency Symbols 21
 
< 0.1%
Specials 3
 
< 0.1%

Most frequent character per block

ASCII
ValueCountFrequency (%)
17103010
17.5%
e 9191411
 
9.4%
a 6276097
 
6.4%
o 6082761
 
6.2%
t 5903172
 
6.0%
i 5412363
 
5.5%
s 4971246
 
5.1%
r 4714610
 
4.8%
n 4135560
 
4.2%
h 3493882
 
3.6%
Other values (93) 30311228
31.1%
Punctuation
ValueCountFrequency (%)
5030
54.2%
1191
 
12.8%
1176
 
12.7%
605
 
6.5%
602
 
6.5%
408
 
4.4%
195
 
2.1%
66
 
0.7%
3
 
< 0.1%
2
 
< 0.1%
Other values (2) 3
 
< 0.1%
None
ValueCountFrequency (%)
½ 49
24.0%
¿ 48
23.5%
ï 48
23.5%
é 6
 
2.9%
œ 5
 
2.5%
¹ 3
 
1.5%
® 3
 
1.5%
è 3
 
1.5%
å 3
 
1.5%
â 3
 
1.5%
Other values (25) 33
16.2%
Letterlike Symbols
ValueCountFrequency (%)
38
100.0%
Currency Symbols
ValueCountFrequency (%)
21
100.0%
Specials
ValueCountFrequency (%)
3
100.0%

Images
Text

Distinct165889
Distinct (%)31.7%
Missing1
Missing (%)< 0.1%
Memory size4.0 MiB
2024-01-05T16:21:53.394815image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

Length

Max length60705
Median length12
Mean length106.23674
Min length12

Characters and Unicode

Total characters55510397
Distinct characters93
Distinct categories13 ?
Distinct scripts2 ?
Distinct blocks3 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique165880 ?
Unique (%)31.7%

Sample

1st rowc("https://img.sndimg.com/food/image/upload/w_555,h_416,c_fit,fl_progressive,q_95/v1/img/recipes/38/YUeirxMLQaeE1h3v3qnM_229%20berry%20blue%20frzn%20dess.jpg", "https://img.sndimg.com/food/image/upload/w_555,h_416,c_fit,fl_progressive,q_95/v1/img/recipes/38/AFPDDHATWzQ0b1CDpDAT_255%20berry%20blue%20frzn%20dess.jpg", "https://img.sndimg.com/food/image/upload/w_555,h_416,c_fit,fl_progressive,q_95/v1/img/recipes/38/UYgf9nwMT2SGGJCuzILO_228%20berry%20blue%20frzn%20dess.jpg", "https://img.sndimg.com/food/image/upload/w_555,h_416,c_fit,fl_progressive,q_95/v1/img/recipes/38/PeBMJN2TGSaYks2759BA_20140722_202142.jpg", "https://img.sndimg.com/food/image/upload/w_555,h_416,c_fit,fl_progressive,q_95/v1/img/recipes/38/picuaETeN.jpg", "https://img.sndimg.com/food/image/upload/w_555,h_416,c_fit,fl_progressive,q_95/v1/img/recipes/38/pictzvxW5.jpg")
2nd rowc("https://img.sndimg.com/food/image/upload/w_555,h_416,c_fit,fl_progressive,q_95/v1/img/recipes/39/picM9Mhnw.jpg", "https://img.sndimg.com/food/image/upload/w_555,h_416,c_fit,fl_progressive,q_95/v1/img/recipes/39/picHv4Ocr.jpg")
3rd rowc("https://img.sndimg.com/food/image/upload/w_555,h_416,c_fit,fl_progressive,q_95/v1/img/recipes/40/picJ4Sz3N.jpg", "https://img.sndimg.com/food/image/upload/w_555,h_416,c_fit,fl_progressive,q_95/v1/img/recipes/40/pic23FWio.jpg")
4th rowc("https://img.sndimg.com/food/image/upload/w_555,h_416,c_fit,fl_progressive,q_95/v1/img/recipes/41/picmbLig8.jpg", "https://img.sndimg.com/food/image/upload/w_555,h_416,c_fit,fl_progressive,q_95/v1/img/recipes/41/picL02w0s.jpg")
5th row"https://img.sndimg.com/food/image/upload/w_555,h_416,c_fit,fl_progressive,q_95/v1/img/recipes/42/picVEMxk8.jpg"
ValueCountFrequency (%)
character(0 356620
45.8%
8253
 
1.1%
site-3.jpg 151
 
< 0.1%
site-2.jpg 150
 
< 0.1%
s.jpg 147
 
< 0.1%
2).jpg 101
 
< 0.1%
and 97
 
< 0.1%
step 95
 
< 0.1%
site.jpg 91
 
< 0.1%
with 67
 
< 0.1%
Other values (408922) 413276
53.0%
2024-01-05T16:21:53.712990image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
/ 5280556
 
9.5%
i 3279625
 
5.9%
e 2530721
 
4.6%
g 2502546
 
4.5%
c 2416152
 
4.4%
p 2409164
 
4.3%
o 2143836
 
3.9%
s 2135009
 
3.8%
m 2130512
 
3.8%
_ 2122895
 
3.8%
Other values (83) 28559381
51.4%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 33184081
59.8%
Other Punctuation 9623566
 
17.3%
Decimal Number 7559500
 
13.6%
Connector Punctuation 2122895
 
3.8%
Uppercase Letter 1777400
 
3.2%
Open Punctuation 441633
 
0.8%
Close Punctuation 441631
 
0.8%
Space Separator 248312
 
0.4%
Dash Punctuation 79259
 
0.1%
Control 32002
 
0.1%
Other values (3) 118
 
< 0.1%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
i 3279625
 
9.9%
e 2530721
 
7.6%
g 2502546
 
7.5%
c 2416152
 
7.3%
p 2409164
 
7.3%
o 2143836
 
6.5%
s 2135009
 
6.4%
m 2130512
 
6.4%
r 2009481
 
6.1%
t 1666613
 
5.0%
Other values (18) 9960422
30.0%
Uppercase Letter
ValueCountFrequency (%)
S 97671
 
5.5%
G 90470
 
5.1%
C 82838
 
4.7%
T 80131
 
4.5%
R 79032
 
4.4%
Q 78544
 
4.4%
P 78489
 
4.4%
A 73785
 
4.2%
J 72039
 
4.1%
D 69975
 
3.9%
Other values (16) 974426
54.8%
Other Punctuation
ValueCountFrequency (%)
/ 5280556
54.9%
, 1847461
 
19.2%
. 1230902
 
12.8%
" 817980
 
8.5%
: 409001
 
4.2%
% 37459
 
0.4%
? 70
 
< 0.1%
# 58
 
< 0.1%
! 33
 
< 0.1%
' 20
 
< 0.1%
Other values (3) 26
 
< 0.1%
Decimal Number
ValueCountFrequency (%)
5 1924653
25.5%
1 1261150
16.7%
4 770892
10.2%
0 751054
 
9.9%
6 706959
 
9.4%
9 701230
 
9.3%
2 458917
 
6.1%
3 382259
 
5.1%
8 302385
 
4.0%
7 300001
 
4.0%
Open Punctuation
ValueCountFrequency (%)
( 441619
> 99.9%
[ 13
 
< 0.1%
{ 1
 
< 0.1%
Math Symbol
ValueCountFrequency (%)
= 60
52.2%
~ 51
44.3%
+ 4
 
3.5%
Close Punctuation
ValueCountFrequency (%)
) 441617
> 99.9%
] 14
 
< 0.1%
Dash Punctuation
ValueCountFrequency (%)
- 79258
> 99.9%
1
 
< 0.1%
Final Punctuation
ValueCountFrequency (%)
1
50.0%
1
50.0%
Connector Punctuation
ValueCountFrequency (%)
_ 2122895
100.0%
Space Separator
ValueCountFrequency (%)
248312
100.0%
Control
ValueCountFrequency (%)
32002
100.0%
Initial Punctuation
ValueCountFrequency (%)
1
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 34961481
63.0%
Common 20548916
37.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
i 3279625
 
9.4%
e 2530721
 
7.2%
g 2502546
 
7.2%
c 2416152
 
6.9%
p 2409164
 
6.9%
o 2143836
 
6.1%
s 2135009
 
6.1%
m 2130512
 
6.1%
r 2009481
 
5.7%
t 1666613
 
4.8%
Other values (44) 11737822
33.6%
Common
ValueCountFrequency (%)
/ 5280556
25.7%
_ 2122895
10.3%
5 1924653
 
9.4%
, 1847461
 
9.0%
1 1261150
 
6.1%
. 1230902
 
6.0%
" 817980
 
4.0%
4 770892
 
3.8%
0 751054
 
3.7%
6 706959
 
3.4%
Other values (29) 3834414
18.7%

Most occurring blocks

ValueCountFrequency (%)
ASCII 55510391
> 99.9%
Punctuation 4
 
< 0.1%
None 2
 
< 0.1%

Most frequent character per block

ASCII
ValueCountFrequency (%)
/ 5280556
 
9.5%
i 3279625
 
5.9%
e 2530721
 
4.6%
g 2502546
 
4.5%
c 2416152
 
4.4%
p 2409164
 
4.3%
o 2143836
 
3.9%
s 2135009
 
3.8%
m 2130512
 
3.8%
_ 2122895
 
3.8%
Other values (77) 28559375
51.4%
Punctuation
ValueCountFrequency (%)
1
25.0%
1
25.0%
1
25.0%
1
25.0%
None
ValueCountFrequency (%)
ñ 1
50.0%
é 1
50.0%
Distinct311
Distinct (%)0.1%
Missing751
Missing (%)0.1%
Memory size4.0 MiB
2024-01-05T16:21:53.919959image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

Length

Max length28
Median length20
Mean length8.6726387
Min length3

Characters and Unicode

Total characters4525088
Distinct characters65
Distinct categories8 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique27 ?
Unique (%)< 0.1%

Sample

1st rowFrozen Desserts
2nd rowChicken Breast
3rd rowBeverages
4th rowSoy/Tofu
5th rowVegetable
ValueCountFrequency (%)
dessert 62072
 
8.3%
lunch/snacks 32586
 
4.3%
32109
 
4.3%
dish 31346
 
4.2%
one 31345
 
4.2%
meal 31345
 
4.2%
breads 28934
 
3.9%
chicken 27858
 
3.7%
vegetable 27231
 
3.6%
mins 25401
 
3.4%
Other values (340) 418884
55.9%
2024-01-05T16:21:54.160490image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
e 660486
14.6%
s 427048
 
9.4%
a 346830
 
7.7%
r 267625
 
5.9%
n 233048
 
5.2%
t 232970
 
5.1%
227345
 
5.0%
i 190053
 
4.2%
o 162516
 
3.6%
h 143635
 
3.2%
Other values (55) 1633532
36.1%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 3440676
76.0%
Uppercase Letter 725289
 
16.0%
Space Separator 227345
 
5.0%
Decimal Number 55771
 
1.2%
Other Punctuation 44971
 
1.0%
Math Symbol 30370
 
0.7%
Open Punctuation 333
 
< 0.1%
Close Punctuation 333
 
< 0.1%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
e 660486
19.2%
s 427048
12.4%
a 346830
10.1%
r 267625
7.8%
n 233048
 
6.8%
t 232970
 
6.8%
i 190053
 
5.5%
o 162516
 
4.7%
h 143635
 
4.2%
c 140219
 
4.1%
Other values (16) 636246
18.5%
Uppercase Letter
ValueCountFrequency (%)
D 110497
15.2%
B 100666
13.9%
S 88559
12.2%
C 84610
11.7%
M 75838
10.5%
P 56801
7.8%
L 54598
7.5%
O 36246
 
5.0%
V 32099
 
4.4%
H 11468
 
1.6%
Other values (15) 73907
10.2%
Decimal Number
ValueCountFrequency (%)
0 18739
33.6%
6 9719
17.4%
3 9020
16.2%
1 6662
 
11.9%
5 6662
 
11.9%
4 4969
 
8.9%
Other Punctuation
ValueCountFrequency (%)
/ 38680
86.0%
. 4521
 
10.1%
& 1739
 
3.9%
' 31
 
0.1%
Space Separator
ValueCountFrequency (%)
227345
100.0%
Math Symbol
ValueCountFrequency (%)
< 30370
100.0%
Open Punctuation
ValueCountFrequency (%)
( 333
100.0%
Close Punctuation
ValueCountFrequency (%)
) 333
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 4165965
92.1%
Common 359123
 
7.9%

Most frequent character per script

Latin
ValueCountFrequency (%)
e 660486
15.9%
s 427048
 
10.3%
a 346830
 
8.3%
r 267625
 
6.4%
n 233048
 
5.6%
t 232970
 
5.6%
i 190053
 
4.6%
o 162516
 
3.9%
h 143635
 
3.4%
c 140219
 
3.4%
Other values (41) 1361535
32.7%
Common
ValueCountFrequency (%)
227345
63.3%
/ 38680
 
10.8%
< 30370
 
8.5%
0 18739
 
5.2%
6 9719
 
2.7%
3 9020
 
2.5%
1 6662
 
1.9%
5 6662
 
1.9%
4 4969
 
1.4%
. 4521
 
1.3%
Other values (4) 2436
 
0.7%

Most occurring blocks

ValueCountFrequency (%)
ASCII 4525088
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
e 660486
14.6%
s 427048
 
9.4%
a 346830
 
7.7%
r 267625
 
5.9%
n 233048
 
5.2%
t 232970
 
5.1%
227345
 
5.0%
i 190053
 
4.2%
o 162516
 
3.6%
h 143635
 
3.2%
Other values (55) 1633532
36.1%

Keywords
Text

MISSING 

Distinct216569
Distinct (%)42.9%
Missing17237
Missing (%)3.3%
Memory size4.0 MiB
2024-01-05T16:21:54.312191image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

Length

Max length420
Median length275
Mean length61.932536
Min length6

Characters and Unicode

Total characters31293272
Distinct characters67
Distinct categories8 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique186613 ?
Unique (%)36.9%

Sample

1st rowc("Dessert", "Low Protein", "Low Cholesterol", "Healthy", "Free Of...", "Summer", "Weeknight", "Freezer", "Easy")
2nd rowc("Chicken Thigh & Leg", "Chicken", "Poultry", "Meat", "Asian", "Indian", "Weeknight", "Stove Top")
3rd rowc("Low Protein", "Low Cholesterol", "Healthy", "Summer", "< 60 Mins")
4th rowc("Beans", "Vegetable", "Low Cholesterol", "Weeknight", "Broil/Grill", "Oven")
5th rowc("Low Protein", "Vegan", "Low Cholesterol", "Healthy", "Winter", "< 60 Mins", "Easy")
ValueCountFrequency (%)
452801
 
11.0%
mins 351158
 
8.5%
easy 276176
 
6.7%
60 149589
 
3.6%
low 125981
 
3.1%
30 112229
 
2.7%
4 111071
 
2.7%
hours 111071
 
2.7%
15 89340
 
2.2%
cook 78169
 
1.9%
Other values (597) 2265396
54.9%
2024-01-05T16:21:54.516360image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
" 5058264
16.2%
3617701
 
11.6%
e 2122123
 
6.8%
, 2023852
 
6.5%
n 1336931
 
4.3%
s 1331210
 
4.3%
i 1225620
 
3.9%
r 1219287
 
3.9%
o 1190001
 
3.8%
a 1078166
 
3.4%
Other values (57) 11090117
35.4%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 14993007
47.9%
Other Punctuation 7282528
23.3%
Space Separator 3617701
 
11.6%
Uppercase Letter 3191418
 
10.2%
Decimal Number 813387
 
2.6%
Open Punctuation 466501
 
1.5%
Close Punctuation 466501
 
1.5%
Math Symbol 462229
 
1.5%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
e 2122123
14.2%
n 1336931
 
8.9%
s 1331210
 
8.9%
i 1225620
 
8.2%
r 1219287
 
8.1%
o 1190001
 
7.9%
a 1078166
 
7.2%
t 883576
 
5.9%
c 704491
 
4.7%
l 558528
 
3.7%
Other values (16) 3343074
22.3%
Uppercase Letter
ValueCountFrequency (%)
M 486077
15.2%
E 337584
10.6%
C 312333
9.8%
H 236285
 
7.4%
F 234359
 
7.3%
L 226749
 
7.1%
B 210552
 
6.6%
S 205430
 
6.4%
P 182406
 
5.7%
V 118453
 
3.7%
Other values (15) 641190
20.1%
Other Punctuation
ValueCountFrequency (%)
" 5058264
69.5%
, 2023852
27.8%
. 150102
 
2.1%
& 29506
 
0.4%
/ 19400
 
0.3%
' 1404
 
< 0.1%
Decimal Number
ValueCountFrequency (%)
0 261818
32.2%
6 149589
18.4%
3 112229
13.8%
4 111071
13.7%
1 89340
 
11.0%
5 89340
 
11.0%
Space Separator
ValueCountFrequency (%)
3617701
100.0%
Open Punctuation
ValueCountFrequency (%)
( 466501
100.0%
Close Punctuation
ValueCountFrequency (%)
) 466501
100.0%
Math Symbol
ValueCountFrequency (%)
< 462229
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 18184425
58.1%
Common 13108847
41.9%

Most frequent character per script

Latin
ValueCountFrequency (%)
e 2122123
 
11.7%
n 1336931
 
7.4%
s 1331210
 
7.3%
i 1225620
 
6.7%
r 1219287
 
6.7%
o 1190001
 
6.5%
a 1078166
 
5.9%
t 883576
 
4.9%
c 704491
 
3.9%
l 558528
 
3.1%
Other values (41) 6534492
35.9%
Common
ValueCountFrequency (%)
" 5058264
38.6%
3617701
27.6%
, 2023852
15.4%
( 466501
 
3.6%
) 466501
 
3.6%
< 462229
 
3.5%
0 261818
 
2.0%
. 150102
 
1.1%
6 149589
 
1.1%
3 112229
 
0.9%
Other values (6) 340061
 
2.6%

Most occurring blocks

ValueCountFrequency (%)
ASCII 31293272
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
" 5058264
16.2%
3617701
 
11.6%
e 2122123
 
6.8%
, 2023852
 
6.5%
n 1336931
 
4.3%
s 1331210
 
4.3%
i 1225620
 
3.9%
r 1219287
 
3.9%
o 1190001
 
3.8%
a 1078166
 
3.4%
Other values (57) 11090117
35.4%
Distinct459571
Distinct (%)88.0%
Missing3
Missing (%)< 0.1%
Memory size4.0 MiB
2024-01-05T16:21:54.732973image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

Length

Max length277
Median length223
Mean length54.92974
Min length3

Characters and Unicode

Total characters28701558
Distinct characters27
Distinct categories9 ?
Distinct scripts2 ?
Distinct blocks2 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique438434 ?
Unique (%)83.9%

Sample

1st rowc("4", "1/4", "1", "1")
2nd rowc("1", "4", "2", "2", "8", "1/4", "8", "1/2", "1", "1", "1/4", "1/4", "1/2", "1/4", "2", "3", NA, "2", "1", "1", "8", "2", "1/3", "1/3", "1/3", "6")
3rd rowc("1 1/2", "1", NA, "1 1/2", NA, "3/4")
4th rowc("12", "1", "2", "1", "10", "1", "3", "2", "2", "2", "1", "2", "1/2", "1/4", "4")
5th rowc("46", "4", "1", "2", "1")
ValueCountFrequency (%)
1 1517767
28.8%
1/2 766346
14.6%
2 734087
14.0%
1/4 391062
 
7.4%
na 291380
 
5.5%
3 238942
 
4.5%
c("1 193232
 
3.7%
4 176360
 
3.4%
3/4 111832
 
2.1%
c("2 98657
 
1.9%
Other values (742) 742567
14.1%
2024-01-05T16:21:54.990116image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
" 9167450
31.9%
4743295
16.5%
, 4359934
15.2%
1 3158692
 
11.0%
2 1735431
 
6.0%
/ 1493319
 
5.2%
4 763225
 
2.7%
3 532745
 
1.9%
c 522511
 
1.8%
) 522491
 
1.8%
Other values (17) 1702465
 
5.9%

Most occurring categories

ValueCountFrequency (%)
Other Punctuation 15023833
52.3%
Decimal Number 6672840
23.2%
Space Separator 4743295
 
16.5%
Uppercase Letter 597406
 
2.1%
Lowercase Letter 522651
 
1.8%
Close Punctuation 522491
 
1.8%
Open Punctuation 522491
 
1.8%
Dash Punctuation 96550
 
0.3%
Math Symbol 1
 
< 0.1%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
1 3158692
47.3%
2 1735431
26.0%
4 763225
 
11.4%
3 532745
 
8.0%
8 129129
 
1.9%
0 120135
 
1.8%
6 112991
 
1.7%
5 96471
 
1.4%
7 16748
 
0.3%
9 7273
 
0.1%
Lowercase Letter
ValueCountFrequency (%)
c 522511
> 99.9%
a 40
 
< 0.1%
r 40
 
< 0.1%
h 20
 
< 0.1%
t 20
 
< 0.1%
e 20
 
< 0.1%
Other Punctuation
ValueCountFrequency (%)
" 9167450
61.0%
, 4359934
29.0%
/ 1493319
 
9.9%
. 3130
 
< 0.1%
Uppercase Letter
ValueCountFrequency (%)
N 298703
50.0%
A 298703
50.0%
Space Separator
ValueCountFrequency (%)
4743295
100.0%
Close Punctuation
ValueCountFrequency (%)
) 522491
100.0%
Open Punctuation
ValueCountFrequency (%)
( 522491
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 96550
100.0%
Math Symbol
ValueCountFrequency (%)
1
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 27581501
96.1%
Latin 1120057
 
3.9%

Most frequent character per script

Common
ValueCountFrequency (%)
" 9167450
33.2%
4743295
17.2%
, 4359934
15.8%
1 3158692
 
11.5%
2 1735431
 
6.3%
/ 1493319
 
5.4%
4 763225
 
2.8%
3 532745
 
1.9%
) 522491
 
1.9%
( 522491
 
1.9%
Other values (9) 582428
 
2.1%
Latin
ValueCountFrequency (%)
c 522511
46.7%
N 298703
26.7%
A 298703
26.7%
a 40
 
< 0.1%
r 40
 
< 0.1%
h 20
 
< 0.1%
t 20
 
< 0.1%
e 20
 
< 0.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII 28701557
> 99.9%
Punctuation 1
 
< 0.1%

Most frequent character per block

ASCII
ValueCountFrequency (%)
" 9167450
31.9%
4743295
16.5%
, 4359934
15.2%
1 3158692
 
11.0%
2 1735431
 
6.0%
/ 1493319
 
5.2%
4 763225
 
2.7%
3 532745
 
1.9%
c 522511
 
1.8%
) 522491
 
1.8%
Other values (16) 1702464
 
5.9%
Punctuation
ValueCountFrequency (%)
1
100.0%
Distinct497120
Distinct (%)95.1%
Missing0
Missing (%)0.0%
Memory size4.0 MiB
2024-01-05T16:21:55.178926image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

Length

Max length810
Median length479
Mean length116.45049
Min length5

Characters and Unicode

Total characters60847359
Distinct characters83
Distinct categories12 ?
Distinct scripts2 ?
Distinct blocks3 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique486841 ?
Unique (%)93.2%

Sample

1st rowc("blueberries", "granulated sugar", "vanilla yogurt", "lemon juice")
2nd rowc("saffron", "milk", "hot green chili peppers", "onions", "garlic", "clove", "peppercorns", "cardamom seed", "cumin seed", "poppy seed", "mace", "cilantro", "mint leaf", "fresh lemon juice", "plain yogurt", "boneless chicken", "salt", "ghee", "onion", "tomatoes", "basmati rice", "long-grain rice", "raisins", "cashews", "eggs")
3rd rowc("sugar", "lemons, rind of", "lemon, zest of", "fresh water", "fresh lemon juice")
4th rowc("extra firm tofu", "eggplant", "zucchini", "mushrooms", "soy sauce", "low sodium soy sauce", "olive oil", "maple syrup", "honey", "red wine vinegar", "lemon juice", "garlic cloves", "mustard powder", "black pepper")
5th rowc("plain tomato juice", "cabbage", "onion", "carrots", "celery")
ValueCountFrequency (%)
salt 232763
 
3.3%
sugar 220300
 
3.1%
pepper 193891
 
2.7%
cheese 162473
 
2.3%
fresh 148617
 
2.1%
garlic 142782
 
2.0%
butter 142369
 
2.0%
flour 126852
 
1.8%
onion 122142
 
1.7%
ground 115514
 
1.6%
Other values (4867) 5460021
77.3%
2024-01-05T16:21:55.398257image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
" 8258868
13.6%
6545298
 
10.8%
e 5154498
 
8.5%
, 3619498
 
5.9%
r 3571499
 
5.9%
a 3548789
 
5.8%
s 2876504
 
4.7%
o 2827593
 
4.6%
l 2501424
 
4.1%
c 2473936
 
4.1%
Other values (73) 19469452
32.0%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 41141554
67.6%
Other Punctuation 11892713
 
19.5%
Space Separator 6545298
 
10.8%
Open Punctuation 514096
 
0.8%
Close Punctuation 514093
 
0.8%
Uppercase Letter 117154
 
0.2%
Dash Punctuation 115993
 
0.2%
Decimal Number 6159
 
< 0.1%
Other Symbol 203
 
< 0.1%
Control 90
 
< 0.1%
Other values (2) 6
 
< 0.1%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
e 5154498
12.5%
r 3571499
 
8.7%
a 3548789
 
8.6%
s 2876504
 
7.0%
o 2827593
 
6.9%
l 2501424
 
6.1%
c 2473936
 
6.0%
n 2399459
 
5.8%
i 2345481
 
5.7%
t 2148927
 
5.2%
Other values (16) 11293444
27.5%
Uppercase Letter
ValueCountFrequency (%)
W 23799
20.3%
D 12805
10.9%
C 12536
10.7%
S 9087
 
7.8%
B 8191
 
7.0%
T 7957
 
6.8%
I 7175
 
6.1%
M 5880
 
5.0%
A 4321
 
3.7%
E 3723
 
3.2%
Other values (16) 21680
18.5%
Other Punctuation
ValueCountFrequency (%)
" 8258868
69.4%
, 3619498
30.4%
' 9699
 
0.1%
% 3056
 
< 0.1%
& 1332
 
< 0.1%
; 92
 
< 0.1%
/ 71
 
< 0.1%
! 45
 
< 0.1%
. 26
 
< 0.1%
# 11
 
< 0.1%
Other values (3) 15
 
< 0.1%
Decimal Number
ValueCountFrequency (%)
0 2389
38.8%
2 1812
29.4%
1 1525
24.8%
8 122
 
2.0%
6 112
 
1.8%
9 93
 
1.5%
5 58
 
0.9%
7 29
 
0.5%
3 14
 
0.2%
4 5
 
0.1%
Space Separator
ValueCountFrequency (%)
6545298
100.0%
Open Punctuation
ValueCountFrequency (%)
( 514096
100.0%
Close Punctuation
ValueCountFrequency (%)
) 514093
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 115993
100.0%
Other Symbol
ValueCountFrequency (%)
® 203
100.0%
Control
ValueCountFrequency (%)
90
100.0%
Connector Punctuation
ValueCountFrequency (%)
_ 3
100.0%
Final Punctuation
ValueCountFrequency (%)
3
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 41258708
67.8%
Common 19588651
32.2%

Most frequent character per script

Latin
ValueCountFrequency (%)
e 5154498
12.5%
r 3571499
 
8.7%
a 3548789
 
8.6%
s 2876504
 
7.0%
o 2827593
 
6.9%
l 2501424
 
6.1%
c 2473936
 
6.0%
n 2399459
 
5.8%
i 2345481
 
5.7%
t 2148927
 
5.2%
Other values (42) 11410598
27.7%
Common
ValueCountFrequency (%)
" 8258868
42.2%
6545298
33.4%
, 3619498
18.5%
( 514096
 
2.6%
) 514093
 
2.6%
- 115993
 
0.6%
' 9699
 
< 0.1%
% 3056
 
< 0.1%
0 2389
 
< 0.1%
2 1812
 
< 0.1%
Other values (21) 3849
 
< 0.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII 60847153
> 99.9%
None 203
 
< 0.1%
Punctuation 3
 
< 0.1%

Most frequent character per block

ASCII
ValueCountFrequency (%)
" 8258868
13.6%
6545298
 
10.8%
e 5154498
 
8.5%
, 3619498
 
5.9%
r 3571499
 
5.9%
a 3548789
 
5.8%
s 2876504
 
4.7%
o 2827593
 
4.6%
l 2501424
 
4.1%
c 2473936
 
4.1%
Other values (71) 19469246
32.0%
None
ValueCountFrequency (%)
® 203
100.0%
Punctuation
ValueCountFrequency (%)
3
100.0%

AggregatedRating
Real number (ℝ)

MISSING 

Distinct9
Distinct (%)< 0.1%
Missing253223
Missing (%)48.5%
Infinite0
Infinite (%)0.0%
Mean4.6320137
Minimum1
Maximum5
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size4.0 MiB
2024-01-05T16:21:55.462759image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile3
Q14.5
median5
Q35
95-th percentile5
Maximum5
Range4
Interquartile range (IQR)0.5

Descriptive statistics

Standard deviation0.64193411
Coefficient of variation (CV)0.1385864
Kurtosis7.7807116
Mean4.6320137
Median Absolute Deviation (MAD)0
Skewness-2.4478663
Sum1247373.5
Variance0.4120794
MonotonicityNot monotonic
2024-01-05T16:21:55.495434image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram with fixed size bins (bins=9)
ValueCountFrequency (%)
5 174516
33.4%
4 42829
 
8.2%
4.5 34330
 
6.6%
3 9166
 
1.8%
3.5 3978
 
0.8%
2 2049
 
0.4%
1 1677
 
0.3%
2.5 673
 
0.1%
1.5 76
 
< 0.1%
(Missing) 253223
48.5%
ValueCountFrequency (%)
1 1677
 
0.3%
1.5 76
 
< 0.1%
2 2049
 
0.4%
2.5 673
 
0.1%
3 9166
 
1.8%
3.5 3978
 
0.8%
4 42829
 
8.2%
4.5 34330
 
6.6%
5 174516
33.4%
ValueCountFrequency (%)
5 174516
33.4%
4.5 34330
 
6.6%
4 42829
 
8.2%
3.5 3978
 
0.8%
3 9166
 
1.8%
2.5 673
 
0.1%
2 2049
 
0.4%
1.5 76
 
< 0.1%
1 1677
 
0.3%

ReviewCount
Real number (ℝ)

MISSING  SKEWED 

Distinct420
Distinct (%)0.2%
Missing247489
Missing (%)47.4%
Infinite0
Infinite (%)0.0%
Mean5.2277841
Minimum1
Maximum3063
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size4.0 MiB
2024-01-05T16:21:55.535414image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1
Q11
median2
Q34
95-th percentile16
Maximum3063
Range3062
Interquartile range (IQR)3

Descriptive statistics

Standard deviation20.381347
Coefficient of variation (CV)3.8986589
Kurtosis3679.1306
Mean5.2277841
Median Absolute Deviation (MAD)1
Skewness42.510881
Sum1437787
Variance415.39931
MonotonicityNot monotonic
2024-01-05T16:21:55.575872image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1 107055
20.5%
2 53212
 
10.2%
3 29906
 
5.7%
4 18702
 
3.6%
5 12814
 
2.5%
6 9449
 
1.8%
7 7156
 
1.4%
8 5336
 
1.0%
9 4156
 
0.8%
10 3223
 
0.6%
Other values (410) 24019
 
4.6%
(Missing) 247489
47.4%
ValueCountFrequency (%)
1 107055
20.5%
2 53212
10.2%
3 29906
 
5.7%
4 18702
 
3.6%
5 12814
 
2.5%
6 9449
 
1.8%
7 7156
 
1.4%
8 5336
 
1.0%
9 4156
 
0.8%
10 3223
 
0.6%
ValueCountFrequency (%)
3063 1
< 0.1%
2273 1
< 0.1%
1692 1
< 0.1%
1657 1
< 0.1%
1586 1
< 0.1%
1410 1
< 0.1%
1409 1
< 0.1%
1384 1
< 0.1%
1326 1
< 0.1%
1284 1
< 0.1%

Calories
Real number (ℝ)

HIGH CORRELATION  SKEWED 

Distinct30138
Distinct (%)5.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean484.43858
Minimum0
Maximum612854.6
Zeros3111
Zeros (%)0.6%
Negative0
Negative (%)0.0%
Memory size4.0 MiB
2024-01-05T16:21:55.618322image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile55
Q1174.2
median317.1
Q3529.1
95-th percentile1305.8
Maximum612854.6
Range612854.6
Interquartile range (IQR)354.9

Descriptive statistics

Standard deviation1397.1166
Coefficient of variation (CV)2.8839913
Kurtosis96111.462
Mean484.43858
Median Absolute Deviation (MAD)165
Skewness252.21445
Sum2.5312739 × 108
Variance1951934.9
MonotonicityNot monotonic
2024-01-05T16:21:55.658851image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 3111
 
0.6%
174.2 150
 
< 0.1%
156.2 150
 
< 0.1%
105 143
 
< 0.1%
104 134
 
< 0.1%
228.2 133
 
< 0.1%
226.2 133
 
< 0.1%
218.2 133
 
< 0.1%
133 132
 
< 0.1%
181.8 130
 
< 0.1%
Other values (30128) 518168
99.2%
ValueCountFrequency (%)
0 3111
0.6%
0.1 59
 
< 0.1%
0.2 27
 
< 0.1%
0.3 26
 
< 0.1%
0.4 32
 
< 0.1%
0.5 17
 
< 0.1%
0.6 63
 
< 0.1%
0.7 25
 
< 0.1%
0.8 37
 
< 0.1%
0.9 26
 
< 0.1%
ValueCountFrequency (%)
612854.6 1
< 0.1%
434360.2 1
< 0.1%
350473.1 1
< 0.1%
101614.7 1
< 0.1%
90904.2 1
< 0.1%
70396.6 1
< 0.1%
57933.7 1
< 0.1%
54097.4 1
< 0.1%
51193.4 1
< 0.1%
45609 1
< 0.1%

FatContent
Real number (ℝ)

HIGH CORRELATION  SKEWED  ZEROS 

Distinct4523
Distinct (%)0.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean24.614922
Minimum0
Maximum64368.1
Zeros11340
Zeros (%)2.2%
Negative0
Negative (%)0.0%
Memory size4.0 MiB
2024-01-05T16:21:55.704507image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0.2
Q15.6
median13.8
Q327.4
95-th percentile75.6
Maximum64368.1
Range64368.1
Interquartile range (IQR)21.8

Descriptive statistics

Standard deviation111.4858
Coefficient of variation (CV)4.5291957
Kurtosis222874.33
Mean24.614922
Median Absolute Deviation (MAD)9.7
Skewness410.5726
Sum12861715
Variance12429.083
MonotonicityNot monotonic
2024-01-05T16:21:55.745250image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 11340
 
2.2%
0.1 8843
 
1.7%
0.2 6385
 
1.2%
0.3 4788
 
0.9%
0.4 3411
 
0.7%
0.5 3199
 
0.6%
0.6 2842
 
0.5%
0.8 2492
 
0.5%
0.7 2465
 
0.5%
7 2240
 
0.4%
Other values (4513) 474512
90.8%
ValueCountFrequency (%)
0 11340
2.2%
0.1 8843
1.7%
0.2 6385
1.2%
0.3 4788
0.9%
0.4 3411
 
0.7%
0.5 3199
 
0.6%
0.6 2842
 
0.5%
0.7 2465
 
0.5%
0.8 2492
 
0.5%
0.9 1978
 
0.4%
ValueCountFrequency (%)
64368.1 1
< 0.1%
30123.7 1
< 0.1%
11169.4 1
< 0.1%
9491 1
< 0.1%
7963.4 1
< 0.1%
4701.1 1
< 0.1%
4012.1 1
< 0.1%
3368.9 1
< 0.1%
2994.9 1
< 0.1%
2817.5 1
< 0.1%

SaturatedFatContent
Real number (ℝ)

HIGH CORRELATION  SKEWED  ZEROS 

Distinct2533
Distinct (%)0.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean9.5594572
Minimum0
Maximum26740.6
Zeros27584
Zeros (%)5.3%
Negative0
Negative (%)0.0%
Memory size4.0 MiB
2024-01-05T16:21:55.788174image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q11.5
median4.7
Q310.8
95-th percentile30.2
Maximum26740.6
Range26740.6
Interquartile range (IQR)9.3

Descriptive statistics

Standard deviation46.622621
Coefficient of variation (CV)4.8771201
Kurtosis219834.56
Mean9.5594572
Median Absolute Deviation (MAD)3.8
Skewness409.77433
Sum4994978.9
Variance2173.6688
MonotonicityNot monotonic
2024-01-05T16:21:55.831638image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 27584
 
5.3%
0.1 16300
 
3.1%
0.2 8490
 
1.6%
0.5 7150
 
1.4%
0.3 7123
 
1.4%
0.7 7079
 
1.4%
0.8 6849
 
1.3%
0.6 6695
 
1.3%
1.1 6568
 
1.3%
0.4 6422
 
1.2%
Other values (2523) 422257
80.8%
ValueCountFrequency (%)
0 27584
5.3%
0.1 16300
3.1%
0.2 8490
 
1.6%
0.3 7123
 
1.4%
0.4 6422
 
1.2%
0.5 7150
 
1.4%
0.6 6695
 
1.3%
0.7 7079
 
1.4%
0.8 6849
 
1.3%
0.9 6385
 
1.2%
ValueCountFrequency (%)
26740.6 1
< 0.1%
13269.4 1
< 0.1%
5869.7 1
< 0.1%
2079 1
< 0.1%
1502 1
< 0.1%
1375.2 1
< 0.1%
1277.1 1
< 0.1%
1261 1
< 0.1%
1253.9 1
< 0.1%
1100.4 1
< 0.1%

CholesterolContent
Real number (ℝ)

HIGH CORRELATION  SKEWED  ZEROS 

Distinct9803
Distinct (%)1.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean86.487003
Minimum0
Maximum130456.4
Zeros110399
Zeros (%)21.1%
Negative0
Negative (%)0.0%
Memory size4.0 MiB
2024-01-05T16:21:55.873604image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q13.8
median42.6
Q3107.9
95-th percentile291.5
Maximum130456.4
Range130456.4
Interquartile range (IQR)104.1

Descriptive statistics

Standard deviation301.98701
Coefficient of variation (CV)3.4917039
Kurtosis93008.477
Mean86.487003
Median Absolute Deviation (MAD)42.6
Skewness250.14014
Sum45190929
Variance91196.154
MonotonicityNot monotonic
2024-01-05T16:21:55.920091image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 110399
 
21.1%
30.5 2502
 
0.5%
15.3 2486
 
0.5%
7.6 2142
 
0.4%
10.2 1762
 
0.3%
68.4 1455
 
0.3%
77.1 1417
 
0.3%
92.8 1249
 
0.2%
5.1 1226
 
0.2%
20.3 1219
 
0.2%
Other values (9793) 396660
75.9%
ValueCountFrequency (%)
0 110399
21.1%
0.1 1117
 
0.2%
0.2 930
 
0.2%
0.3 851
 
0.2%
0.4 818
 
0.2%
0.5 662
 
0.1%
0.6 852
 
0.2%
0.7 535
 
0.1%
0.8 729
 
0.1%
0.9 428
 
0.1%
ValueCountFrequency (%)
130456.4 1
 
< 0.1%
89892 1
 
< 0.1%
83404.5 1
 
< 0.1%
37224 1
 
< 0.1%
11857 1
 
< 0.1%
11823.8 3
< 0.1%
10135.6 1
 
< 0.1%
9856.4 1
 
< 0.1%
9167.2 1
 
< 0.1%
8976.3 1
 
< 0.1%

SodiumContent
Real number (ℝ)

HIGH CORRELATION  SKEWED 

Distinct40455
Distinct (%)7.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean767.26388
Minimum0
Maximum1246921.1
Zeros3029
Zeros (%)0.6%
Negative0
Negative (%)0.0%
Memory size4.0 MiB
2024-01-05T16:21:55.964186image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile7.5
Q1123.3
median353.3
Q3792.2
95-th percentile2217.1
Maximum1246921.1
Range1246921.1
Interquartile range (IQR)668.9

Descriptive statistics

Standard deviation4203.6205
Coefficient of variation (CV)5.4787155
Kurtosis20475.802
Mean767.26388
Median Absolute Deviation (MAD)277.3
Skewness102.24327
Sum4.0090842 × 108
Variance17670426
MonotonicityNot monotonic
2024-01-05T16:21:56.009508image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 3029
 
0.6%
0.6 633
 
0.1%
0.3 603
 
0.1%
0.9 532
 
0.1%
0.8 518
 
0.1%
1.4 482
 
0.1%
0.1 474
 
0.1%
1.2 474
 
0.1%
0.4 468
 
0.1%
1.1 440
 
0.1%
Other values (40445) 514864
98.5%
ValueCountFrequency (%)
0 3029
0.6%
0.1 474
 
0.1%
0.2 383
 
0.1%
0.3 603
 
0.1%
0.4 468
 
0.1%
0.5 440
 
0.1%
0.6 633
 
0.1%
0.7 411
 
0.1%
0.8 518
 
0.1%
0.9 532
 
0.1%
ValueCountFrequency (%)
1246921.1 1
< 0.1%
731056.4 1
< 0.1%
704129.6 1
< 0.1%
528051.8 1
< 0.1%
452942.4 1
< 0.1%
448059.7 1
< 0.1%
387832 1
< 0.1%
381983 1
< 0.1%
351950.2 1
< 0.1%
349356.4 1
< 0.1%

CarbohydrateContent
Real number (ℝ)

HIGH CORRELATION  SKEWED  ZEROS 

Distinct8102
Distinct (%)1.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean49.089092
Minimum0
Maximum108294.6
Zeros5693
Zeros (%)1.1%
Negative0
Negative (%)0.0%
Memory size4.0 MiB
2024-01-05T16:21:56.053340image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile2.4
Q112.8
median28.2
Q351.1
95-th percentile136.8
Maximum108294.6
Range108294.6
Interquartile range (IQR)38.3

Descriptive statistics

Standard deviation180.82206
Coefficient of variation (CV)3.6835487
Kurtosis245890.35
Mean49.089092
Median Absolute Deviation (MAD)17.7
Skewness413.82702
Sum25649885
Variance32696.618
MonotonicityNot monotonic
2024-01-05T16:21:56.096113image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 5693
 
1.1%
8.2 1182
 
0.2%
5 1164
 
0.2%
8.8 1164
 
0.2%
6 1161
 
0.2%
9.2 1152
 
0.2%
6.5 1143
 
0.2%
13.2 1140
 
0.2%
5.2 1140
 
0.2%
6.2 1138
 
0.2%
Other values (8092) 506440
96.9%
ValueCountFrequency (%)
0 5693
1.1%
0.1 860
 
0.2%
0.2 725
 
0.1%
0.3 685
 
0.1%
0.4 767
 
0.1%
0.5 797
 
0.2%
0.6 772
 
0.1%
0.7 900
 
0.2%
0.8 1027
 
0.2%
0.9 890
 
0.2%
ValueCountFrequency (%)
108294.6 1
< 0.1%
13409.6 1
< 0.1%
9023.8 1
< 0.1%
8737.9 1
< 0.1%
7695.8 1
< 0.1%
6945.5 1
< 0.1%
6826.2 1
< 0.1%
6699.7 1
< 0.1%
6170.4 1
< 0.1%
5602.7 1
< 0.1%

FiberContent
Real number (ℝ)

HIGH CORRELATION  SKEWED  ZEROS 

Distinct1067
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean3.843242
Minimum0
Maximum3012
Zeros27001
Zeros (%)5.2%
Negative0
Negative (%)0.0%
Memory size4.0 MiB
2024-01-05T16:21:56.139444image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10.8
median2.2
Q34.6
95-th percentile12.7
Maximum3012
Range3012
Interquartile range (IQR)3.8

Descriptive statistics

Standard deviation8.6031628
Coefficient of variation (CV)2.238517
Kurtosis34219.191
Mean3.843242
Median Absolute Deviation (MAD)1.6
Skewness118.70034
Sum2008159.3
Variance74.01441
MonotonicityNot monotonic
2024-01-05T16:21:56.190153image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 27001
 
5.2%
0.1 16494
 
3.2%
0.2 14478
 
2.8%
0.3 13547
 
2.6%
0.8 13335
 
2.6%
0.6 13106
 
2.5%
0.5 12848
 
2.5%
0.4 12058
 
2.3%
0.7 11970
 
2.3%
1.1 11780
 
2.3%
Other values (1057) 375900
71.9%
ValueCountFrequency (%)
0 27001
5.2%
0.1 16494
3.2%
0.2 14478
2.8%
0.3 13547
2.6%
0.4 12058
2.3%
0.5 12848
2.5%
0.6 13106
2.5%
0.7 11970
2.3%
0.8 13335
2.6%
0.9 11590
2.2%
ValueCountFrequency (%)
3012 1
< 0.1%
1748.6 1
< 0.1%
1503.2 1
< 0.1%
835.7 1
< 0.1%
799.9 1
< 0.1%
704.7 1
< 0.1%
560.2 1
< 0.1%
548.6 1
< 0.1%
519.5 1
< 0.1%
472 1
< 0.1%

SugarContent
Real number (ℝ)

HIGH CORRELATION  SKEWED  ZEROS 

Distinct6008
Distinct (%)1.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean21.878254
Minimum0
Maximum90682.3
Zeros11802
Zeros (%)2.3%
Negative0
Negative (%)0.0%
Memory size4.0 MiB
2024-01-05T16:21:56.247138image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0.2
Q12.5
median6.4
Q317.9
95-th percentile71.1
Maximum90682.3
Range90682.3
Interquartile range (IQR)15.4

Descriptive statistics

Standard deviation142.62019
Coefficient of variation (CV)6.5188105
Kurtosis312540.15
Mean21.878254
Median Absolute Deviation (MAD)5.1
Skewness493.47054
Sum11431760
Variance20340.519
MonotonicityNot monotonic
2024-01-05T16:21:56.298172image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 11802
 
2.3%
0.1 8519
 
1.6%
0.2 6388
 
1.2%
0.3 5610
 
1.1%
1.6 5218
 
1.0%
1.1 5164
 
1.0%
0.8 5126
 
1.0%
0.4 5044
 
1.0%
0.6 5029
 
1.0%
1.4 4978
 
1.0%
Other values (5998) 459639
88.0%
ValueCountFrequency (%)
0 11802
2.3%
0.1 8519
1.6%
0.2 6388
1.2%
0.3 5610
1.1%
0.4 5044
1.0%
0.5 4870
0.9%
0.6 5029
1.0%
0.7 4649
 
0.9%
0.8 5126
1.0%
0.9 4623
 
0.9%
ValueCountFrequency (%)
90682.3 1
< 0.1%
7565.2 1
< 0.1%
4735.8 1
< 0.1%
4730 1
< 0.1%
4570.9 1
< 0.1%
4553.1 1
< 0.1%
4531.8 1
< 0.1%
4326.7 1
< 0.1%
4225.3 1
< 0.1%
4153.6 1
< 0.1%

ProteinContent
Real number (ℝ)

HIGH CORRELATION  SKEWED  ZEROS 

Distinct2581
Distinct (%)0.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean17.46951
Minimum0
Maximum18396.2
Zeros8403
Zeros (%)1.6%
Negative0
Negative (%)0.0%
Memory size4.0 MiB
2024-01-05T16:21:56.339341image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0.5
Q13.5
median9.1
Q325
95-th percentile54.1
Maximum18396.2
Range18396.2
Interquartile range (IQR)21.5

Descriptive statistics

Standard deviation40.128837
Coefficient of variation (CV)2.2970785
Kurtosis87281.35
Mean17.46951
Median Absolute Deviation (MAD)7.3
Skewness208.64792
Sum9128116.1
Variance1610.3235
MonotonicityNot monotonic
2024-01-05T16:21:56.598078image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 8403
 
1.6%
0.1 4883
 
0.9%
1.1 4066
 
0.8%
1.4 4057
 
0.8%
2.5 3896
 
0.7%
0.8 3877
 
0.7%
1.9 3876
 
0.7%
1.6 3843
 
0.7%
0.2 3792
 
0.7%
4 3751
 
0.7%
Other values (2571) 478073
91.5%
ValueCountFrequency (%)
0 8403
1.6%
0.1 4883
0.9%
0.2 3792
0.7%
0.3 3513
0.7%
0.4 2951
 
0.6%
0.5 3411
0.7%
0.6 3465
0.7%
0.7 3464
0.7%
0.8 3877
0.7%
0.9 3637
0.7%
ValueCountFrequency (%)
18396.2 1
< 0.1%
7454.9 1
< 0.1%
4437.2 1
< 0.1%
3278.3 1
< 0.1%
3276.2 1
< 0.1%
3270.3 2
< 0.1%
2454.5 1
< 0.1%
2340.6 1
< 0.1%
2178.2 1
< 0.1%
2106.8 1
< 0.1%

RecipeServings
Real number (ℝ)

MISSING  SKEWED 

Distinct171
Distinct (%)0.1%
Missing182911
Missing (%)35.0%
Infinite0
Infinite (%)0.0%
Mean8.6061907
Minimum1
Maximum32767
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size4.0 MiB
2024-01-05T16:21:56.641759image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1
Q14
median6
Q38
95-th percentile24
Maximum32767
Range32766
Interquartile range (IQR)4

Descriptive statistics

Standard deviation114.31981
Coefficient of variation (CV)13.283439
Kurtosis79442.859
Mean8.6061907
Median Absolute Deviation (MAD)2
Skewness277.97933
Sum2922714
Variance13069.019
MonotonicityNot monotonic
2024-01-05T16:21:56.683075image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
4 102893
19.7%
6 61721
 
11.8%
8 44894
 
8.6%
12 26914
 
5.2%
1 20821
 
4.0%
2 19898
 
3.8%
10 12987
 
2.5%
16 8260
 
1.6%
24 7444
 
1.4%
5 4515
 
0.9%
Other values (161) 29259
 
5.6%
(Missing) 182911
35.0%
ValueCountFrequency (%)
1 20821
 
4.0%
2 19898
 
3.8%
3 3602
 
0.7%
4 102893
19.7%
5 4515
 
0.9%
6 61721
11.8%
7 831
 
0.2%
8 44894
8.6%
9 2303
 
0.4%
10 12987
 
2.5%
ValueCountFrequency (%)
32767 4
< 0.1%
5000 2
< 0.1%
3800 2
< 0.1%
3500 1
 
< 0.1%
2000 1
 
< 0.1%
1296 1
 
< 0.1%
1280 1
 
< 0.1%
1215 1
 
< 0.1%
1210 1
 
< 0.1%
1200 1
 
< 0.1%

RecipeYield
Text

MISSING 

Distinct34043
Distinct (%)19.5%
Missing348071
Missing (%)66.6%
Memory size4.0 MiB
2024-01-05T16:21:56.833472image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

Length

Max length56
Median length53
Mean length9.50904
Min length1

Characters and Unicode

Total characters1658814
Distinct characters107
Distinct categories14 ?
Distinct scripts2 ?
Distinct blocks3 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique25418 ?
Unique (%)14.6%

Sample

1st row4 kebabs
2nd row1 9-inch pie
3rd row1 cup
4th row84 cookies
5th row1 pie
ValueCountFrequency (%)
1 52780
 
13.2%
cups 23489
 
5.9%
2 18536
 
4.6%
4 14292
 
3.6%
12 11361
 
2.8%
cookies 11155
 
2.8%
cup 9115
 
2.3%
1/2 8971
 
2.2%
6 8742
 
2.2%
8 8535
 
2.1%
Other values (6552) 232485
58.2%
2024-01-05T16:21:57.055145image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
225104
 
13.6%
s 143124
 
8.6%
e 108493
 
6.5%
1 96379
 
5.8%
c 87744
 
5.3%
a 84133
 
5.1%
o 76733
 
4.6%
i 72714
 
4.4%
p 71732
 
4.3%
u 62243
 
3.8%
Other values (97) 630415
38.0%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 1092599
65.9%
Decimal Number 284677
 
17.2%
Space Separator 225104
 
13.6%
Uppercase Letter 22351
 
1.3%
Other Punctuation 17058
 
1.0%
Dash Punctuation 14491
 
0.9%
Open Punctuation 1257
 
0.1%
Close Punctuation 1226
 
0.1%
Math Symbol 36
 
< 0.1%
Modifier Symbol 7
 
< 0.1%
Other values (4) 8
 
< 0.1%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
s 143124
13.1%
e 108493
 
9.9%
c 87744
 
8.0%
a 84133
 
7.7%
o 76733
 
7.0%
i 72714
 
6.7%
p 71732
 
6.6%
u 62243
 
5.7%
n 50740
 
4.6%
l 50281
 
4.6%
Other values (29) 284662
26.1%
Uppercase Letter
ValueCountFrequency (%)
C 4904
21.9%
P 2658
11.9%
S 2492
11.1%
B 2349
10.5%
L 1461
 
6.5%
M 1050
 
4.7%
T 969
 
4.3%
D 879
 
3.9%
R 646
 
2.9%
E 637
 
2.8%
Other values (17) 4306
19.3%
Other Punctuation
ValueCountFrequency (%)
/ 13407
78.6%
. 1807
 
10.6%
" 651
 
3.8%
, 651
 
3.8%
' 351
 
2.1%
? 52
 
0.3%
! 50
 
0.3%
& 38
 
0.2%
; 18
 
0.1%
: 15
 
0.1%
Other values (3) 18
 
0.1%
Decimal Number
ValueCountFrequency (%)
1 96379
33.9%
2 59082
20.8%
4 34872
 
12.2%
6 22036
 
7.7%
3 21526
 
7.6%
8 17380
 
6.1%
0 17271
 
6.1%
5 9098
 
3.2%
9 4699
 
1.7%
7 2334
 
0.8%
Math Symbol
ValueCountFrequency (%)
+ 23
63.9%
= 6
 
16.7%
~ 3
 
8.3%
| 2
 
5.6%
× 2
 
5.6%
Open Punctuation
ValueCountFrequency (%)
( 1252
99.6%
[ 5
 
0.4%
Close Punctuation
ValueCountFrequency (%)
) 1225
99.9%
] 1
 
0.1%
Modifier Symbol
ValueCountFrequency (%)
` 6
85.7%
¨ 1
 
14.3%
Final Punctuation
ValueCountFrequency (%)
2
66.7%
1
33.3%
Space Separator
ValueCountFrequency (%)
225104
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 14491
100.0%
Other Symbol
ValueCountFrequency (%)
© 3
100.0%
Other Number
ValueCountFrequency (%)
½ 1
100.0%
Control
ValueCountFrequency (%)
 1
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 1114950
67.2%
Common 543864
32.8%

Most frequent character per script

Latin
ValueCountFrequency (%)
s 143124
12.8%
e 108493
 
9.7%
c 87744
 
7.9%
a 84133
 
7.5%
o 76733
 
6.9%
i 72714
 
6.5%
p 71732
 
6.4%
u 62243
 
5.6%
n 50740
 
4.6%
l 50281
 
4.5%
Other values (56) 307013
27.5%
Common
ValueCountFrequency (%)
225104
41.4%
1 96379
17.7%
2 59082
 
10.9%
4 34872
 
6.4%
6 22036
 
4.1%
3 21526
 
4.0%
8 17380
 
3.2%
0 17271
 
3.2%
- 14491
 
2.7%
/ 13407
 
2.5%
Other values (31) 22316
 
4.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII 1658755
> 99.9%
None 56
 
< 0.1%
Punctuation 3
 
< 0.1%

Most frequent character per block

ASCII
ValueCountFrequency (%)
225104
 
13.6%
s 143124
 
8.6%
e 108493
 
6.5%
1 96379
 
5.8%
c 87744
 
5.3%
a 84133
 
5.1%
o 76733
 
4.6%
i 72714
 
4.4%
p 71732
 
4.3%
u 62243
 
3.8%
Other values (77) 630356
38.0%
None
ValueCountFrequency (%)
é 15
26.8%
á 5
 
8.9%
ê 5
 
8.9%
à 4
 
7.1%
à 4
 
7.1%
ñ 4
 
7.1%
è 3
 
5.4%
© 3
 
5.4%
× 2
 
3.6%
û 2
 
3.6%
Other values (8) 9
16.1%
Punctuation
ValueCountFrequency (%)
2
66.7%
1
33.3%
Distinct519993
Distinct (%)99.5%
Missing0
Missing (%)0.0%
Memory size4.0 MiB
2024-01-05T16:21:57.493944image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

Length

Max length12709
Median length3845
Mean length594.26251
Min length2

Characters and Unicode

Total characters310512262
Distinct characters205
Distinct categories19 ?
Distinct scripts2 ?
Distinct blocks5 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique518213 ?
Unique (%)99.2%

Sample

1st rowc("Toss 2 cups berries with sugar.", "Let stand for 45 minutes, stirring occasionally.", "Transfer berry-sugar mixture to food processor.", "Add yogurt and process until smooth.", "Strain through fine sieve. Pour into baking pan (or transfer to ice cream maker and process according to manufacturers' directions). Freeze uncovered until edges are solid but centre is soft. Transfer to processor and blend until smooth again.", "Return to pan and freeze until edges are solid.", "Transfer to processor and blend until smooth again.", "Fold in remaining 2 cups of blueberries.", "Pour into plastic mold and freeze overnight. Let soften slightly to serve.")
2nd rowc("Soak saffron in warm milk for 5 minutes and puree in blender.", "Add chiles, onions, ginger, garlic, cloves, peppercorns, cardamom seeds, cinnamon, coriander and cumin seeds, poppy seeds, nutmeg, mace, cilantro or mint leaves and lemon juice. Blend into smooth paste. Put paste into large bowl, add yogurt and mix well.", "Marinate chicken in yogurt mixture with salt, covered for at least 2 - 6 hours in refrigerator.", "In skillet. heat oil over medium heat for 1 minute. Add ghee and 15 seconds later add onion and fry for about8 minutes.", "Reserve for garnish.", "In same skillet, cook chicken with its marinade with tomatoes for about 10 minutes over medium heat, uncovered.", "Remove chicken pieces from the sauce and set aside. Add rice to sauce, bring to boil, and cook, covered over low heat for 15 minutes.", "Return chicken and add raisins, cashews and almonds; mix well.", "Simmer, covered for 5 minutes.", "Place chicken, eggs and rice in large serving dish in such a way that yellow of the eggs, the saffron-colored rice, the nuts and the chicken make a colorful display.", "Add reserved onion as garnish.")
3rd rowc("Into a 1 quart Jar with tight fitting lid, put sugar and lemon peel, or zest; add 1 1/2 cups very hot water (not from tap!). With lid fitted firmly, shake jar until sugar is dissolved.", "Add lemon juice. Refrigerate until chilled.", "To Serve: Into each 12-ounce glass, over ice cubes, pour 1/4 cup of the lemon syrup.", "Then add chilled club soda or, if you prefer, water.", "Stir to mix well.")
4th rowc("Drain the tofu, carefully squeezing out excess water, and pat dry with paper towels.", "Cut tofu into one-inch squares.", "Set aside. Cut eggplant lengthwise in half, then cut each half into approximately three strips.", "Cut strips crosswise into one-inch cubes.", "Slice zucchini into half-inch thick slices.", "Cut red pepper in half, removing stem and seeds, and cut each half into one-inch squares.", "Wipe mushrooms clean with a moist paper towel and remove stems.", "Thread tofu and vegetables on to barbecue skewers in alternating color combinations: For example, first a piece of eggplant, then a slice of tofu, then zucchini, then red pepper, baby corn and mushrooms.", "Continue in this way until all skewers are full.", "Make the marinade by putting all ingredients in a blender, and blend on high speed for about one minute until mixed.", "Alternatively, put all ingredients in a glass jar, cover tightly with the lid and shake well until mixed.", "Lay the kebabs in a long, shallow baking pan or on a non-metal tray, making sure they lie flat. Evenly pour the marinade over the kebabs, turning them once so that the tofu and vegetables are coated.", "Refrigerate the kebabs for three to eight hours, occasionally spooning the marinade over them.", "Broil or grill the kebabs at 450 F for 15-20 minutes, or on the grill, until the vegetables are browned.", "Suggestions This meal can be served over cooked, brown rice. Amounts can easily be doubled to make four servings.")
5th rowc("Mix everything together and bring to a boil.", "Reduce heat and simmer for 30 minutes (longer if you prefer your veggies to be soft).", "Refrigerate until cool.", "Serve chilled with sour cream.")
ValueCountFrequency (%)
and 2657337
 
5.0%
the 2588412
 
4.9%
a 1394715
 
2.6%
to 1384546
 
2.6%
in 1331758
 
2.5%
with 897145
 
1.7%
until 802738
 
1.5%
add 732045
 
1.4%
of 708115
 
1.3%
minutes 678493
 
1.3%
Other values (168317) 39977379
75.2%
2024-01-05T16:21:57.829786image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
53114812
17.1%
e 27280893
 
8.8%
t 19700060
 
6.3%
o 18061782
 
5.8%
a 17827147
 
5.7%
n 16754039
 
5.4%
i 16446715
 
5.3%
r 14806185
 
4.8%
s 12613101
 
4.1%
l 10954278
 
3.5%
Other values (195) 102953250
33.2%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 225873609
72.7%
Space Separator 53117925
 
17.1%
Other Punctuation 18904704
 
6.1%
Uppercase Letter 6127495
 
2.0%
Decimal Number 3891062
 
1.3%
Close Punctuation 775517
 
0.2%
Open Punctuation 771428
 
0.2%
Dash Punctuation 595320
 
0.2%
Control 326042
 
0.1%
Other Symbol 78448
 
< 0.1%
Other values (9) 50712
 
< 0.1%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
e 27280893
12.1%
t 19700060
 
8.7%
o 18061782
 
8.0%
a 17827147
 
7.9%
n 16754039
 
7.4%
i 16446715
 
7.3%
r 14806185
 
6.6%
s 12613101
 
5.6%
l 10954278
 
4.8%
d 9561550
 
4.2%
Other values (50) 61867859
27.4%
Uppercase Letter
ValueCountFrequency (%)
S 806513
13.2%
A 616086
10.1%
P 612656
10.0%
C 606227
9.9%
I 433706
 
7.1%
B 409303
 
6.7%
T 404641
 
6.6%
R 350456
 
5.7%
M 284422
 
4.6%
F 259664
 
4.2%
Other values (40) 1343821
21.9%
Other Punctuation
ValueCountFrequency (%)
" 7005417
37.1%
, 5933962
31.4%
. 4913224
26.0%
; 308988
 
1.6%
/ 278795
 
1.5%
: 124533
 
0.7%
' 95682
 
0.5%
\ 70729
 
0.4%
! 66808
 
0.4%
& 57260
 
0.3%
Other values (14) 49306
 
0.3%
Decimal Number
ValueCountFrequency (%)
1 876576
22.5%
0 634677
16.3%
2 611470
15.7%
5 572949
14.7%
3 528119
13.6%
4 316737
 
8.1%
8 111415
 
2.9%
6 86298
 
2.2%
7 78656
 
2.0%
9 74165
 
1.9%
Math Symbol
ValueCountFrequency (%)
+ 2911
25.5%
~ 2733
24.0%
> 1804
15.8%
= 1784
15.6%
< 1607
14.1%
× 490
 
4.3%
| 59
 
0.5%
± 10
 
0.1%
¬ 10
 
0.1%
÷ 3
 
< 0.1%
Other Number
ValueCountFrequency (%)
½ 8643
65.1%
¼ 3508
26.4%
¾ 1104
 
8.3%
³ 18
 
0.1%
¹ 11
 
0.1%
² 2
 
< 0.1%
Modifier Symbol
ValueCountFrequency (%)
` 453
56.7%
^ 206
25.8%
´ 81
 
10.1%
¨ 54
 
6.8%
¸ 4
 
0.5%
¯ 1
 
0.1%
Open Punctuation
ValueCountFrequency (%)
( 769567
99.8%
[ 1399
 
0.2%
{ 404
 
0.1%
51
 
< 0.1%
7
 
< 0.1%
Other Symbol
ValueCountFrequency (%)
° 77328
98.6%
® 1034
 
1.3%
© 67
 
0.1%
¦ 18
 
< 0.1%
1
 
< 0.1%
Currency Symbol
ValueCountFrequency (%)
$ 181
90.0%
£ 9
 
4.5%
7
 
3.5%
¢ 3
 
1.5%
¤ 1
 
0.5%
Control
ValueCountFrequency (%)
326032
> 99.9%
‘ 4
 
< 0.1%
4
 
< 0.1%
2
 
< 0.1%
Final Punctuation
ValueCountFrequency (%)
11091
74.7%
3755
 
25.3%
» 7
 
< 0.1%
4
 
< 0.1%
Initial Punctuation
ValueCountFrequency (%)
1552
82.8%
318
 
17.0%
3
 
0.2%
« 1
 
0.1%
Close Punctuation
ValueCountFrequency (%)
) 773716
99.8%
] 1395
 
0.2%
} 406
 
0.1%
Dash Punctuation
ValueCountFrequency (%)
- 589060
98.9%
4709
 
0.8%
1551
 
0.3%
Space Separator
ValueCountFrequency (%)
53114812
> 99.9%
  3113
 
< 0.1%
Other Letter
ValueCountFrequency (%)
º 6993
99.9%
ª 7
 
0.1%
Connector Punctuation
ValueCountFrequency (%)
_ 1177
100.0%
Format
ValueCountFrequency (%)
­ 107
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 232008104
74.7%
Common 78504158
 
25.3%

Most frequent character per script

Latin
ValueCountFrequency (%)
e 27280893
11.8%
t 19700060
 
8.5%
o 18061782
 
7.8%
a 17827147
 
7.7%
n 16754039
 
7.2%
i 16446715
 
7.1%
r 14806185
 
6.4%
s 12613101
 
5.4%
l 10954278
 
4.7%
d 9561550
 
4.1%
Other values (102) 68002354
29.3%
Common
ValueCountFrequency (%)
53114812
67.7%
" 7005417
 
8.9%
, 5933962
 
7.6%
. 4913224
 
6.3%
1 876576
 
1.1%
) 773716
 
1.0%
( 769567
 
1.0%
0 634677
 
0.8%
2 611470
 
0.8%
- 589060
 
0.8%
Other values (83) 3281677
 
4.2%

Most occurring blocks

ValueCountFrequency (%)
ASCII 310354793
99.9%
None 132289
 
< 0.1%
Punctuation 25172
 
< 0.1%
Currency Symbols 7
 
< 0.1%
Letterlike Symbols 1
 
< 0.1%

Most frequent character per block

ASCII
ValueCountFrequency (%)
53114812
17.1%
e 27280893
 
8.8%
t 19700060
 
6.3%
o 18061782
 
5.8%
a 17827147
 
5.7%
n 16754039
 
5.4%
i 16446715
 
5.3%
r 14806185
 
4.8%
s 12613101
 
4.1%
l 10954278
 
3.5%
Other values (88) 102795781
33.1%
None
ValueCountFrequency (%)
° 77328
58.5%
é 24358
 
18.4%
½ 8643
 
6.5%
º 6993
 
5.3%
¼ 3508
 
2.7%
  3113
 
2.4%
ñ 1748
 
1.3%
¾ 1104
 
0.8%
è 1095
 
0.8%
® 1034
 
0.8%
Other values (81) 3365
 
2.5%
Punctuation
ValueCountFrequency (%)
11091
44.1%
4709
18.7%
3755
 
14.9%
1552
 
6.2%
1551
 
6.2%
1501
 
6.0%
620
 
2.5%
318
 
1.3%
51
 
0.2%
7
 
< 0.1%
Other values (4) 17
 
0.1%
Currency Symbols
ValueCountFrequency (%)
7
100.0%
Letterlike Symbols
ValueCountFrequency (%)
1
100.0%

Interactions

2024-01-05T16:21:43.812699image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2024-01-05T16:21:29.230985image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
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2024-01-05T16:21:33.770590image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2024-01-05T16:21:34.913636image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2024-01-05T16:21:36.044768image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2024-01-05T16:21:37.255318image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2024-01-05T16:21:38.337012image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2024-01-05T16:21:39.417573image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2024-01-05T16:21:40.685226image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
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2024-01-05T16:21:30.914061image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2024-01-05T16:21:32.016546image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2024-01-05T16:21:33.030226image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2024-01-05T16:21:34.022892image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2024-01-05T16:21:35.125861image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2024-01-05T16:21:36.370978image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2024-01-05T16:21:37.460503image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2024-01-05T16:21:38.547051image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2024-01-05T16:21:39.634369image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2024-01-05T16:21:40.891405image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2024-01-05T16:21:41.915919image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
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2024-01-05T16:21:32.078118image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
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2024-01-05T16:21:36.456648image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
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2024-01-05T16:21:38.629492image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2024-01-05T16:21:39.708421image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2024-01-05T16:21:40.965655image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2024-01-05T16:21:41.988859image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2024-01-05T16:21:43.049010image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2024-01-05T16:21:44.131644image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2024-01-05T16:21:29.769789image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2024-01-05T16:21:31.082808image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2024-01-05T16:21:32.144156image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2024-01-05T16:21:33.160373image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2024-01-05T16:21:34.186380image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
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2024-01-05T16:21:41.037367image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
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2024-01-05T16:21:31.165594image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2024-01-05T16:21:32.212841image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2024-01-05T16:21:33.225526image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
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2024-01-05T16:21:35.368505image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2024-01-05T16:21:36.628576image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2024-01-05T16:21:37.705042image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2024-01-05T16:21:38.781310image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2024-01-05T16:21:39.864297image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2024-01-05T16:21:41.111866image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2024-01-05T16:21:42.153015image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2024-01-05T16:21:43.210086image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
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2024-01-05T16:21:29.930738image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2024-01-05T16:21:31.243443image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2024-01-05T16:21:32.286517image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2024-01-05T16:21:33.292045image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2024-01-05T16:21:34.351426image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2024-01-05T16:21:35.448589image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2024-01-05T16:21:36.704144image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2024-01-05T16:21:37.800192image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2024-01-05T16:21:38.859036image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2024-01-05T16:21:39.941145image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2024-01-05T16:21:41.188020image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2024-01-05T16:21:42.233463image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
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2024-01-05T16:21:44.335085image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2024-01-05T16:21:30.008791image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2024-01-05T16:21:31.321025image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
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2024-01-05T16:21:35.545007image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2024-01-05T16:21:36.783647image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2024-01-05T16:21:37.883032image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2024-01-05T16:21:38.935207image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2024-01-05T16:21:40.017152image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2024-01-05T16:21:41.265768image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2024-01-05T16:21:42.313522image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2024-01-05T16:21:43.367457image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2024-01-05T16:21:44.405671image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2024-01-05T16:21:30.086145image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2024-01-05T16:21:31.398884image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2024-01-05T16:21:32.431333image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2024-01-05T16:21:33.422028image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2024-01-05T16:21:34.521105image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2024-01-05T16:21:35.642251image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2024-01-05T16:21:36.861006image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2024-01-05T16:21:37.964006image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2024-01-05T16:21:39.014860image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
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2024-01-05T16:21:42.456854image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
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2024-01-05T16:21:32.564281image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
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2024-01-05T16:21:37.010584image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2024-01-05T16:21:38.114899image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2024-01-05T16:21:39.169870image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2024-01-05T16:21:40.279280image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
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2024-01-05T16:21:43.677621image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2024-01-05T16:21:44.685981image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
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2024-01-05T16:21:31.752124image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2024-01-05T16:21:32.690419image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
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2024-01-05T16:21:35.968437image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
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2024-01-05T16:21:38.259077image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2024-01-05T16:21:39.333714image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2024-01-05T16:21:40.465103image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2024-01-05T16:21:41.630075image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2024-01-05T16:21:42.663555image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2024-01-05T16:21:43.747051image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

Correlations

2024-01-05T16:21:57.900195image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
AggregatedRatingAuthorIdCaloriesCarbohydrateContentCholesterolContentFatContentFiberContentProteinContentRecipeIdRecipeServingsReviewCountSaturatedFatContentSodiumContentSugarContent
AggregatedRating1.0000.017-0.001-0.015-0.0130.017-0.015-0.0390.0520.0060.1530.017-0.0120.020
AuthorId0.0171.000-0.014-0.008-0.010-0.0070.0140.0050.6840.003-0.196-0.0140.008-0.028
Calories-0.001-0.0141.0000.6600.6130.8330.4590.723-0.020-0.220-0.0170.7530.5810.351
CarbohydrateContent-0.015-0.0080.6601.0000.1560.3200.6220.302-0.018-0.038-0.0140.2940.3160.649
CholesterolContent-0.013-0.0100.6130.1561.0000.6870.0390.708-0.024-0.098-0.0030.7390.4900.045
FatContent0.017-0.0070.8330.3200.6871.0000.2770.635-0.014-0.113-0.0150.9240.5390.153
FiberContent-0.0150.0140.4590.6220.0390.2771.0000.3990.021-0.204-0.0330.1780.3480.293
ProteinContent-0.0390.0050.7230.3020.7080.6350.3991.000-0.006-0.304-0.0030.5670.676-0.017
RecipeId0.0520.684-0.020-0.018-0.024-0.0140.021-0.0061.000-0.012-0.255-0.025-0.000-0.031
RecipeServings0.0060.003-0.220-0.038-0.098-0.113-0.204-0.304-0.0121.000-0.011-0.034-0.1690.104
ReviewCount0.153-0.196-0.017-0.014-0.003-0.015-0.033-0.003-0.255-0.0111.000-0.0080.021-0.019
SaturatedFatContent0.017-0.0140.7530.2940.7390.9240.1780.567-0.025-0.034-0.0081.0000.4810.161
SodiumContent-0.0120.0080.5810.3160.4900.5390.3480.676-0.000-0.1690.0210.4811.0000.050
SugarContent0.020-0.0280.3510.6490.0450.1530.293-0.017-0.0310.104-0.0190.1610.0501.000

Missing values

2024-01-05T16:21:44.928326image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
A simple visualization of nullity by column.
2024-01-05T16:21:45.710748image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.
2024-01-05T16:21:47.897825image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
The correlation heatmap measures nullity correlation: how strongly the presence or absence of one variable affects the presence of another.

Sample

RecipeIdNameAuthorIdAuthorNameCookTimePrepTimeTotalTimeDatePublishedDescriptionImagesRecipeCategoryKeywordsRecipeIngredientQuantitiesRecipeIngredientPartsAggregatedRatingReviewCountCaloriesFatContentSaturatedFatContentCholesterolContentSodiumContentCarbohydrateContentFiberContentSugarContentProteinContentRecipeServingsRecipeYieldRecipeInstructions
038Low-Fat Berry Blue Frozen Dessert1533DancerPT24HPT45MPT24H45M1999-08-09T21:46:00ZMake and share this Low-Fat Berry Blue Frozen Dessert recipe from Food.com.c("https://img.sndimg.com/food/image/upload/w_555,h_416,c_fit,fl_progressive,q_95/v1/img/recipes/38/YUeirxMLQaeE1h3v3qnM_229%20berry%20blue%20frzn%20dess.jpg", "https://img.sndimg.com/food/image/upload/w_555,h_416,c_fit,fl_progressive,q_95/v1/img/recipes/38/AFPDDHATWzQ0b1CDpDAT_255%20berry%20blue%20frzn%20dess.jpg", "https://img.sndimg.com/food/image/upload/w_555,h_416,c_fit,fl_progressive,q_95/v1/img/recipes/38/UYgf9nwMT2SGGJCuzILO_228%20berry%20blue%20frzn%20dess.jpg", "https://img.sndimg.com/food/image/upload/w_555,h_416,c_fit,fl_progressive,q_95/v1/img/recipes/38/PeBMJN2TGSaYks2759BA_20140722_202142.jpg", \n"https://img.sndimg.com/food/image/upload/w_555,h_416,c_fit,fl_progressive,q_95/v1/img/recipes/38/picuaETeN.jpg", "https://img.sndimg.com/food/image/upload/w_555,h_416,c_fit,fl_progressive,q_95/v1/img/recipes/38/pictzvxW5.jpg")Frozen Dessertsc("Dessert", "Low Protein", "Low Cholesterol", "Healthy", "Free Of...", "Summer", "Weeknight", "Freezer", "Easy")c("4", "1/4", "1", "1")c("blueberries", "granulated sugar", "vanilla yogurt", "lemon juice")4.54.0170.92.51.38.029.837.13.630.23.24.0NaNc("Toss 2 cups berries with sugar.", "Let stand for 45 minutes, stirring occasionally.", "Transfer berry-sugar mixture to food processor.", "Add yogurt and process until smooth.", "Strain through fine sieve. Pour into baking pan (or transfer to ice cream maker and process according to manufacturers' directions). Freeze uncovered until edges are solid but centre is soft. Transfer to processor and blend until smooth again.", "Return to pan and freeze until edges are solid.", "Transfer to processor and blend until smooth again.", \n"Fold in remaining 2 cups of blueberries.", "Pour into plastic mold and freeze overnight. Let soften slightly to serve.")
139Biryani1567elly9812PT25MPT4HPT4H25M1999-08-29T13:12:00ZMake and share this Biryani recipe from Food.com.c("https://img.sndimg.com/food/image/upload/w_555,h_416,c_fit,fl_progressive,q_95/v1/img/recipes/39/picM9Mhnw.jpg", "https://img.sndimg.com/food/image/upload/w_555,h_416,c_fit,fl_progressive,q_95/v1/img/recipes/39/picHv4Ocr.jpg")Chicken Breastc("Chicken Thigh & Leg", "Chicken", "Poultry", "Meat", "Asian", "Indian", "Weeknight", "Stove Top")c("1", "4", "2", "2", "8", "1/4", "8", "1/2", "1", "1", "1/4", "1/4", "1/2", "1/4", "2", "3", NA, "2", "1", "1", "8", "2", "1/3", "1/3", "1/3", "6")c("saffron", "milk", "hot green chili peppers", "onions", "garlic", "clove", "peppercorns", "cardamom seed", "cumin seed", "poppy seed", "mace", "cilantro", "mint leaf", "fresh lemon juice", "plain yogurt", "boneless chicken", "salt", "ghee", "onion", "tomatoes", "basmati rice", "long-grain rice", "raisins", "cashews", "eggs")3.01.01110.758.816.6372.8368.484.49.020.463.46.0NaNc("Soak saffron in warm milk for 5 minutes and puree in blender.", "Add chiles, onions, ginger, garlic, cloves, peppercorns, cardamom seeds, cinnamon, coriander and cumin seeds, poppy seeds, nutmeg, mace, cilantro or mint leaves and lemon juice. Blend into smooth paste. Put paste into large bowl, add yogurt and mix well.", "Marinate chicken in yogurt mixture with salt, covered for at least 2 - 6 hours in refrigerator.", "In skillet. heat oil over medium heat for 1 minute. Add ghee and 15 seconds later add onion and fry for about8 minutes.", \n"Reserve for garnish.", "In same skillet, cook chicken with its marinade with tomatoes for about 10 minutes over medium heat, uncovered.", "Remove chicken pieces from the sauce and set aside. Add rice to sauce, bring to boil, and cook, covered over low heat for 15 minutes.", "Return chicken and add raisins, cashews and almonds; mix well.", "Simmer, covered for 5 minutes.", "Place chicken, eggs and rice in large serving dish in such a way that yellow of the eggs, the saffron-colored rice, the nuts and the chicken make a colorful display.", \n"Add reserved onion as garnish.")
240Best Lemonade1566Stephen LittlePT5MPT30MPT35M1999-09-05T19:52:00ZThis is from one of my first Good House Keeping cookbooks. You must use a *zester* in order to avoid getting any of that bitter rind, and when you zest the lemons, zest them onto some sugar from the recipe (the sugar will 'catch' all of the oils). I also advise you from personal experience to use only the best skinned lemons for the best flavor.c("https://img.sndimg.com/food/image/upload/w_555,h_416,c_fit,fl_progressive,q_95/v1/img/recipes/40/picJ4Sz3N.jpg", "https://img.sndimg.com/food/image/upload/w_555,h_416,c_fit,fl_progressive,q_95/v1/img/recipes/40/pic23FWio.jpg")Beveragesc("Low Protein", "Low Cholesterol", "Healthy", "Summer", "< 60 Mins")c("1 1/2", "1", NA, "1 1/2", NA, "3/4")c("sugar", "lemons, rind of", "lemon, zest of", "fresh water", "fresh lemon juice")4.510.0311.10.20.00.01.881.50.477.20.34.0NaNc("Into a 1 quart Jar with tight fitting lid, put sugar and lemon peel, or zest; add 1 1/2 cups very hot water (not from tap!). With lid fitted firmly, shake jar until sugar is dissolved.", "Add lemon juice. Refrigerate until chilled.", "To Serve: Into each 12-ounce glass, over ice cubes, pour 1/4 cup of the lemon syrup.", "Then add chilled club soda or, if you prefer, water.", "Stir to mix well.")
341Carina's Tofu-Vegetable Kebabs1586CyclopzPT20MPT24HPT24H20M1999-09-03T14:54:00ZThis dish is best prepared a day in advance to allow the ingredients to soak in the marinade overnight.c("https://img.sndimg.com/food/image/upload/w_555,h_416,c_fit,fl_progressive,q_95/v1/img/recipes/41/picmbLig8.jpg", "https://img.sndimg.com/food/image/upload/w_555,h_416,c_fit,fl_progressive,q_95/v1/img/recipes/41/picL02w0s.jpg")Soy/Tofuc("Beans", "Vegetable", "Low Cholesterol", "Weeknight", "Broil/Grill", "Oven")c("12", "1", "2", "1", "10", "1", "3", "2", "2", "2", "1", "2", "1/2", "1/4", "4")c("extra firm tofu", "eggplant", "zucchini", "mushrooms", "soy sauce", "low sodium soy sauce", "olive oil", "maple syrup", "honey", "red wine vinegar", "lemon juice", "garlic cloves", "mustard powder", "black pepper")4.52.0536.124.03.80.01558.664.217.332.129.32.04 kebabsc("Drain the tofu, carefully squeezing out excess water, and pat dry with paper towels.", "Cut tofu into one-inch squares.", "Set aside. Cut eggplant lengthwise in half, then cut each half into approximately three strips.", "Cut strips crosswise into one-inch cubes.", "Slice zucchini into half-inch thick slices.", "Cut red pepper in half, removing stem and seeds, and cut each half into one-inch squares.", "Wipe mushrooms clean with a moist paper towel and remove stems.", "Thread tofu and vegetables on to barbecue skewers in alternating color combinations: For example, first a piece of eggplant, then a slice of tofu, then zucchini, then red pepper, baby corn and mushrooms.", \n"Continue in this way until all skewers are full.", "Make the marinade by putting all ingredients in a blender, and blend on high speed for about one minute until mixed.", "Alternatively, put all ingredients in a glass jar, cover tightly with the lid and shake well until mixed.", "Lay the kebabs in a long, shallow baking pan or on a non-metal tray, making sure they lie flat. Evenly pour the marinade over the kebabs, turning them once so that the tofu and vegetables are coated.", "Refrigerate the kebabs for three to eight hours, occasionally spooning the marinade over them.", \n"Broil or grill the kebabs at 450 F for 15-20 minutes, or on the grill, until the vegetables are browned.", "Suggestions This meal can be served over cooked, brown rice. Amounts can easily be doubled to make four servings.")
442Cabbage Soup1538Duckie067PT30MPT20MPT50M1999-09-19T06:19:00ZMake and share this Cabbage Soup recipe from Food.com."https://img.sndimg.com/food/image/upload/w_555,h_416,c_fit,fl_progressive,q_95/v1/img/recipes/42/picVEMxk8.jpg"Vegetablec("Low Protein", "Vegan", "Low Cholesterol", "Healthy", "Winter", "< 60 Mins", "Easy")c("46", "4", "1", "2", "1")c("plain tomato juice", "cabbage", "onion", "carrots", "celery")4.511.0103.60.40.10.0959.325.14.817.74.34.0NaNc("Mix everything together and bring to a boil.", "Reduce heat and simmer for 30 minutes (longer if you prefer your veggies to be soft).", "Refrigerate until cool.", "Serve chilled with sour cream.")
543Best Blackbottom Pie34879Barefoot BeachcomberPT2HPT20MPT2H20M1999-08-21T10:35:00ZMake and share this Best Blackbottom Pie recipe from Food.com.character(0)Piec("Dessert", "Weeknight", "Stove Top", "< 4 Hours")c("1 1/4", "1/4", "6", "1/3", "1/4", "1/4", "2", "3", "1", "1", "1/4", "1", "2", "3", "1/4", "1/2", NA)c("graham cracker crumbs", "sugar", "butter", "sugar", "cornstarch", "salt", "milk", "vanilla extract", "water", "gelatin", "rum", "cream of tartar", "sugar")1.01.0437.919.310.994.3267.658.01.842.57.08.01 9-inch piec("Graham Cracker Crust: In small bowl, combine graham cracker crumbs, sugar and butter. Press evenly on bottom and sides of 9-inch pie plate. Chill until firm (about 1 hour).", "Chocolate Layer: In medium saucepan, combine sugar, cornstarch and salt. Gradually stir in milk. Cook over medium heat, stirring constantly, until mixture boils. Remove from heat. In small bowl, beat egg yolks. Gradually stir in small amount of hot mixture; return to saucepan. Cook over low heat, stirring constantly, for 2 minutes. Remove from heat.", \n"Remove 1-1/2 cups custard to medium bowl; add semi-sweet chocolate morsels and vanilla extract. Stir until morsels are melted and mixture is smooth.", "Pour into prepared Graham Cracker Crust; chill until set (about 30 minutes).", "While Chocolate Layer is chilling, prepare Vanilla Layer.", "Vanilla Layer: In large bowl, combine cold water and gelatin; let stand 5 minutes. Add remaining warm custard; stir until gelatin dissolves. Cool 15 minutes. Stir in rum; beat with wire whisk until smooth. Set aside.", \n"In 1-1/2 quart bowl, combine egg whites and cream of tartar; beat until foamy. Gradually add sugar; beat until stiff peaks form. Fold egg whites into custard; pour over chocolate layer.", "Chill until set (about 2 hours).", "Garnish with whipped cream and chocolate shavings, if desired.", "Makes one 9-inch pie.")
644Warm Chicken A La King1596Joan EdingtonPT3MPT35MPT38M1999-09-17T04:47:00ZI copied this one out of a friend's book so many moons ago that I can't remember where it's from, but it's so decadently fattening that I can't resist pigging out now and then. I usually serve with rice, but I suppose it would go with noodles or new potatoes just as well."https://img.sndimg.com/food/image/upload/w_555,h_416,c_fit,fl_progressive,q_95/v1/img/recipes/44/picsSKvFd.jpg"Chickenc("Poultry", "Meat", "< 60 Mins")c("12", "2", "3", "450", "1", "2", "1/4", "1", NA, NA, "2", "2", "1", NA)c("chicken", "butter", "flour", "milk", "celery", "button mushrooms", "green pepper", "canned pimiento", "salt", "black pepper", "Worcestershire sauce", "parsley")5.023.0895.566.831.9405.8557.229.13.15.045.32.0NaNc("Melt 1 1/2 ozs butter, add the flour and cook for 2 to 3 minutes, stirring.", "Gradually add milk and cook, stirring, until thick and smooth.", "Melt the remaining butter and saute sliced celery, button mushrooms and chopped pepper until soft but not coloured.", "Add celery, mushrooms, pepper, chicken and pimiento to the sauce and heat through.", "Season to taste. Combine the egg yolks, double cream and Worcestershire sauce. Add to the chicken mixture and heat through.", "Transfer to a serving dish and sprinkle with chopped parsley."\n)
745Buttermilk Pie With Gingersnap Crumb Crust1580tristitiaPT50MPT30MPT1H20M1999-08-06T00:40:00ZMake and share this Buttermilk Pie With Gingersnap Crumb Crust recipe from Food.com."https://img.sndimg.com/food/image/upload/w_555,h_416,c_fit,fl_progressive,q_95/v1/img/recipes/45/pic79tPh5.jpg"Piec("Dessert", "Healthy", "Weeknight", "Oven", "< 4 Hours")c("3/4", "1", "1", "2", "3", "1/4", "1", "1/2", "1/2", "2")c("sugar", "margarine", "egg", "flour", "salt", "buttermilk", "graham cracker crumbs", "margarine")4.03.0228.07.11.724.5281.837.50.524.74.28.0NaNc("Preheat oven to 350°F.", "Make pie crust, using 8 inch pie pan, do not bake.", "Mix sugar and margarine in medium bowl until blended; beat in egg whites and egg.", "Stir in flour, salt, and buttermilk until well blended.", "Pour filling into prepared crust, bake 40 minutes or until sharp knife inserted near center comes out clean.", "Sprinkle with nutmeg and serve warm or chilled.", "Combine graham crumbs, gingersnap crumbs, and margarine in 8 or 9 inch pie pan, pat mixture evenly on bottom and side of pan.", \n"Bake 8 to 10 minutes or until edge of crust is lightly browned.", "Cool on wire rack.")
846A Jad - Cucumber Pickle1533DancerNaNPT25MPT25M1999-08-11T19:48:00ZMake and share this A Jad - Cucumber Pickle recipe from Food.com.character(0)Vegetablec("Thai", "Asian", "Free Of...", "< 30 Mins")c("1/2", "5", "2", "1", "1", "1")c("rice vinegar", "haeo")5.02.04.30.00.00.00.71.10.20.20.1NaN1 cupc("Slice the cucumber in four lengthwise, then slice the pieces to segments about an eighth of an inch thick.", "Slice the tops of the chilies (green ones can be used if red are not available, but Thais like the color contrast), tap out any loose seeds and discard, then slice the chilies across into thin rounds.", "Slice the shallots and water chestnuts.", "Combine and serve. This will keep 2 or 3 weeks in a refrigerator.")
947Butter Pecan Cookies1573benlucPT9MPT55MPT1H4M1999-09-07T09:01:00ZMake and share this Butter Pecan Cookies recipe from Food.com.c("https://img.sndimg.com/food/image/upload/w_555,h_416,c_fit,fl_progressive,q_95/v1/img/recipes/47/picfnmxck.jpg", "https://img.sndimg.com/food/image/upload/w_555,h_416,c_fit,fl_progressive,q_95/v1/img/recipes/47/picCPvxZU.jpg")Dessertc("Cookie & Brownie", "Fruit", "Nuts", "Weeknight", "Oven", "< 4 Hours")c("3/4", "1/2", "1", "1", "1", "2", "1")c("butter", "brown sugar", "granulated sugar", "vanilla extract", "flour", "pecan halves")4.02.069.05.61.46.315.04.50.61.60.8NaN84 cookiesc("Preheat oven to 350 degrees.", "Cream butter in large mixing bowl.", "Gradually add brown sugar and granulated sugar.", "Cream well.", "Add unbeaten egg yolk and vanilla and beat well.", "Blend in sifted flour to form a stiff dough.", "Shape dough into small balls.", "Place on greased cookie sheet. Flatten cookies with bottom of glass dipped in sugar.", "Bake at 350 degrees for 7-9 minutes, till golden brown (do not overbrown.) Cool before frosting.", "Garnish with pecan halves.")
RecipeIdNameAuthorIdAuthorNameCookTimePrepTimeTotalTimeDatePublishedDescriptionImagesRecipeCategoryKeywordsRecipeIngredientQuantitiesRecipeIngredientPartsAggregatedRatingReviewCountCaloriesFatContentSaturatedFatContentCholesterolContentSodiumContentCarbohydrateContentFiberContentSugarContentProteinContentRecipeServingsRecipeYieldRecipeInstructions
522507541374MaMa's Bean Salad2002090414rdsxcNaNPT30MPT30M2020-12-21T16:36:00ZMake and share this MaMa's Bean Salad recipe from Food.com.character(0)Vegetablec("Low Cholesterol", "Healthy", "< 30 Mins", "For Large Groups", "Easy")c("1", "1", "1", "1", "1", "1", "1", "1/2", "1/2", "1/4")c("green beans", "English peas", "red bell pepper", "celery", "red onion", "red wine vinegar", "sugar", "canola oil")NaNNaN141.74.00.30.011.722.45.310.05.215.01 Gallonc("Drain and rinse all the beans. (It is unnecessary to rinse the peas, but they do have to be drained.) Sometimes I replace the wax beans with a can of corn. I also add 4 to 8 oz of canned mushrooms, asparagus, and/or artichoke hearts, depending on the crowd. Because the peas are very fragile, drain them last", "Heat sugar and vinegar in a small pan until the sugar is dissolved. Add the oil", "Place the drained beans and chopped vegetables in a large bowl. Reserve the peas. Pour the vinegar over the sala and mix. Add pepper to taste. When salad is mixed to your preference, gently fold in the peas so that they don't get squashed.", \n"Vinegar dressing should cover the salad. If, like me, you have added extra cans of other vegetables, you might need to make another batch of dressing.", "Cover and refrigerate. Salad is best 24 hours after preparation, when the vegetables have had a chance to absorb the dressing. Serve with a slotted spoon.")
522508541375Amazing Ground Beef Stroganoff2002090414rdsxcPT20MPT30MPT50M2020-12-21T16:37:00ZMake and share this Amazing Ground Beef Stroganoff recipe from Food.com.character(0)Meatc("Weeknight", "< 60 Mins", "Beginner Cook", "Easy")c("1", "1", "1", "1/2", "1/4", "1", "1", "1", "1", "1/2")c("hamburger", "onion", "celery", "water chestnut", "dried dill", "mushrooms", "sour cream", "black pepper")NaNNaN422.328.612.6106.0634.714.11.45.727.34.02 Quartsc("Saute meat in a medium skillet until it loses redness. Drain as necessary. Add Better Than Boullion or 1 cube of crushed beef boullion, onion, & celery. Cook until vegetables are limp. Add remaining ingredients and simmer until all ingredients are heated and blended.", "Just before serving, stir in the sour cream and bring back to temperature.", "Serve over rice or noodles.")
522509541376Spanish Coffee with Tia Maria2001004241CLUBFOODYNaNPT10MPT10M2020-12-22T15:12:00ZThis is such a nice digestif to have after your meal! It's the perfect hot cocktail to enjoy during the holidays...\nVIDEO https://youtu.be/gk1ucfe9hug"https://img.sndimg.com/food/image/upload/w_555,h_416,c_fit,fl_progressive,q_95/v1/img/submissions/recipe/2001004241/QBZS0F7zQZC9UfmB46sG_Spanish-Coffee-1_Fotor-1024x768.jpg"Beveragesc("< 15 Mins", "Easy", "From Scratch")c("1", "1", "1", "1 1/2", "6", "3", "1", "1")c("lemon wedge", "granulated sugar", "cognac", "brandy", "maraschino cherry", "ground cinnamon")NaNNaN84.32.11.26.815.716.60.415.40.61.0NaNc("Cut a small slit in the lemon wedge and slide it around the rim of the glass. Dip the rim in granulated sugar and turn back and forth to coat well.", "Add Cognac, Tia Maria and pour in hot coffee. Top generously with whipped cream, sprinkle on some cinnamon and garnish with a cherry. Carefully wrap a serviette around the glass and enjoy! Makes 1 coffee.")
522510541377Slow-Cooker Classic Coffee Cake4769Katrina WynnPT3HPT20MPT3H20M2020-12-22T15:12:00ZYour house will fill with the aromas of cinnamon and butter as this almost-from-scratch coffee cake cooks away in your slow cookercharacter(0)Breads"< 4 Hours"c("1", "1/2", "4", "2", "1/8", "1", "1", "1/2", "4", "1/2", "2 -3", "1/4")c("all-purpose flour", "brown sugar", "butter", "ground cinnamon", "salt", "sour cream", "butter", "eggs", "powdered sugar", "milk", "vanilla")NaNNaN358.919.810.5103.1323.441.50.824.84.812.0NaNc("Line bottom and sides of 5-quart oval slow cooker with single sheet of cooking parchment paper, and spray with cooking spray.", "In medium bowl, stir first five ingredients until crumbly. Set aside.", "In large bowl, stir Cake ingredients (cake mix, sour cream, butter, eggs) until blended. Pour into slow cooker. Place folded, clean dish towel under cover of cooker. This will prevent condensation from dripping down onto cake. Cook on High heat setting 1 hour. Carefully remove slow cooker’s ceramic insert, and rotate insert 180 degrees. Sprinkle topping over cake. Replace cover with dish towel under the cover. Continue to cook on High heat setting 30 minutes to 1 hour or until toothpick inserted in center comes out clean.", \n"Transfer ceramic insert from slow cooker to cooling rack. Let stand 10 minutes. Using parchment paper, carefully lift cake out of ceramic insert, and transfer to cooling rack. Cool completely, about 1 hour. Remove parchment paper.", "In small bowl, beat powdered sugar, milk and vanilla until smooth. Drizzle over cake.", "For an even easier version, you can skip the glaze, and sprinkle powdered sugar on top of cooled cake.", "If preferred, 1/2 cup chopped toasted walnuts or pecans can be mixed into the topping mixture."\n)
522511541378Meg's Pumpkin Spice Bread2001302649Meg J.PT45MPT30MPT1H15M2020-12-22T15:26:00ZEven people who claim they don't like pumpkin love this bread. Popular with family and friends. Smells and tastes yummy.&nbsp;"https://img.sndimg.com/food/image/upload/w_555,h_416,c_fit,fl_progressive,q_95/v1/img/recipes/rz/.3/64/19/5/sAC8h1csTUGgC8Vag9qC_1995014103924154.jpg"Quick Breadsc("Breads", "< 4 Hours", "Easy")c("1", "4", "2/3", "1", "2", "1 1/2", "1 1/2", "1 1/2", "1 1/2", "1", "3 1/2", "3")c("eggs", "water", "pumpkin", "baking soda", "salt", "cinnamon", "ginger", "cloves", "nutmeg", "all-purpose flour", "sugar")NaNNaN898.234.85.2106.3903.3139.02.686.810.8NaN7-8 Mini loavesc("Beat eggs, add oil, water, and pumpkin; mix well. Add baking soda and spices; mix well. Add flour and sugar; mix well.", "Spray eight mini loaf tins with baking Pam. Pour mixture into prepared tins on a large baking sheet about 2/3 full. Keep level and place on middle baking rack for 45 minutes to one hour. When toothpick inserted in middle of loaf comes out clean, they are done. Let cool before removing from tins.", "I wrap and store in non-stick aluminum foil and add a shiny ribbon. Even better the next day. “Freezes beautifully.”."\n)
522512541379Meg's Fresh Ginger Gingerbread2002090414rdsxcPT35MPT1HPT1H35M2020-12-22T15:27:00ZMake and share this Meg's Fresh Ginger Gingerbread recipe from Food.com.character(0)Dessert"< 4 Hours"c("3", "1/2", "1/2", "1/4", "1/4", "1", "1/4", "1 1/2", "1", "1/2", "1/4", "1/2")c("fresh ginger", "unsalted butter", "dark brown sugar", "dark corn syrup", "molasses", "egg", "salt", "all-purpose flour", "baking soda", "cinnamon", "ground cloves", "buttermilk")NaNNaN316.612.57.654.4278.248.50.822.83.98.01 8x8 cake panc("Preheat oven to 350&deg;F Grease an 8x8 cake pan. This recipe uses 2 mixing bowls, 1 large and 1 medium.", "Peel an grate your ginger if not using ginger paste.", "In the large mixing bowl, create together the butter and brown sugar. Add molasses, syrup, egg and salt, beating after each addition. Beat mixture on low speed until smooth.", "In a separate bowl, mix the flour, soda, & spices.", "Mix half the dry mix into the sugar mixture. Beat until smooth", "Mix in 1/4 cup buttermilk. Beat until smooth.", \n"Add remaining flour, then the remaining buttermilk, beating after each addition.", "Use a large spoon to stir in the ginger. Mix until well incorporated.", "Pour into a well greased 8x8 cake pan and back 35 - 40 minutes or until the toothpick jabbed in the middle comes out clean.")
522513541380Roast Prime Rib au Poivre with Mixed Peppercorns211566Denver cooksPT3HPT30MPT3H30M2020-12-22T15:32:00ZWhite, black, green, and pink peppercorns add wonderful flavor to this very special prime rib. If possible, search out a butcher who carries dry-aged beef-it&rsquo;s more tender, flavorful, and juicy than the non-aged variety. A full-bodied California Cabernet Sauvignon or French Bordeaux is the perfect wine to serve. As for vegetables, mix butter and tarragon with cooked baby carrots and green beans for a delicious accompaniment."https://img.sndimg.com/food/image/upload/w_555,h_416,c_fit,fl_progressive,q_95/v1/img/recipes/54/13/80/Qjfc6oF7TOIKkm1mZKmA_rc_rt014-prime-rib-roast-with-rosemary-thyme-au-jus-BLOG-1080x610.png"Very Low Carbsc("High Protein", "High In...", "< 4 Hours")c("9", "2", "4", "2", "1/3", "3 1/2", "1/3")c("Dijon mustard", "garlic", "peppercorns", "shallot", "cognac", "brandy")NaNNaN2063.4172.471.4433.8766.33.20.70.1117.08.01 Roastc("Position rack in center of oven and preheat to 450°F. Place beef, fat side up, in shallow roasting pan. Sprinkle beef with salt. Mix mustard and garlic in small bowl. Spread mustard mixture over top of beef. Sprinkle 2 tablespoons crushed peppercorns over mustard mixture.", "Roast beef 15 minutes. Reduce heat to 325°F. Roast until meat thermometer inserted into center of beef registers 125°F. for medium-rare, tenting loosely with foil if crust browns too quickly, about 2 hours 45 minutes. Transfer beef to platter. Tent with foil to keep warm.", \n"Pour pan juices into 2-cup glass measuring cup (do not clean pan). Freeze juices 10 minutes. Spoon fat off top of pan juices, returning 1-tablespoon fat to roasting pan. Reserve juices.", "Melt fat in same roasting pan over medium-high heat. Add shallots and sauté until tender, scraping up any browned bits from bottom of pan, about 2 minutes. Remove pan from heat. Add canned beef broth, then Cognac (liquid may ignite). Return pan to heat and boil until liquid is reduced to 2 cups, about 15 minutes. Add pan juices and remaining 1 teaspoon crushed peppercorns. Transfer pan juices to sauceboat.", \n"Carve roast and serve with juices.")
522514541381Kirshwasser Ice Cream2001131545Jonathan F.PT3HPT1HPT4H2020-12-22T15:33:00ZMake and share this Kirshwasser Ice Cream recipe from Food.com.character(0)Ice Creamc("Dessert", "< 4 Hours")c("3", "3", "1/2", "1/2", "2", "2")c("half-and-half", "heavy cream", "brandy", "sugar")NaNNaN1271.3117.272.6470.9192.533.90.017.312.86.0NaNc("heat half and half and heavy cream to a simmer add sugar.", "remove from heat beat in two eggs add brandy.", "add food color blend with with blender Place in ice cream maker freeze.", "serve when semi hard cold.")
522515541382Quick & Easy Asian Cucumber Salmon Rolls2001004241CLUBFOODYNaNPT15MPT15M2020-12-22T22:11:00ZExtremely quick and easy to make, these are great hors d'oeuvre to serve at your next party!\nVIDEO https://youtu.be/DMgBU48OvEM"https://img.sndimg.com/food/image/upload/w_555,h_416,c_fit,fl_progressive,q_95/v1/img/submissions/recipe/2001004241/iozyo37LSxy1lG4ujjYh_Asian-Cucumber-8_Fotor-1024x768.jpg"Canadianc("< 15 Mins", "Easy")c("4", "1/4", "1", "1/2", "8 1/2", "40")c("wasabi paste", "dill", "English cucumber", "smoked salmon", "pickled ginger")NaNNaN16.10.60.12.9100.50.30.00.12.4NaN20 rollsc("In a small bowl, combine mayo and wasabi paste. Stir well before adding 1 teaspoons dill; mix well and set aside.", "Leaving the skin on, cut cucumber in half and then each half lengthwise. Using a mandolin, slice the cucumber as thin as possible. Add a thin slice of smoked salmon, slightly smaller than the cucumber on top. Spread a thin layer of dressing on the fish. Add a couple pieces of pickled ginger.", "Roll up cucumber starting on the pickled ginger end. Place each roll on a serving plate and garnish with fresh dill sprigs. Serve immediately.", \n"Note: Depending on the size of your cuke and how thick you slice it, you should get between 8 to 10 slices per quarter section.")
522516541383Spicy Baked Scotch Eggs188099Chef LauraMDPT25MPT15MPT40M2020-12-22T22:12:00ZGreat way to have hard boiled eggs and crispy, spicy sausage! Highly recommend using Jimmy Dean Hot breakfast sausage.&nbsp;character(0)Breakfast"< 60 Mins"c("6 -7", "1", "1/4", "1", "2", "1/2", "1", "1", "1 -2")c("hard-boiled eggs", "breakfast sausage", "panko breadcrumbs", "Worcestershire sauce", "flour", "panko breadcrumbs", "water")NaNNaN1093.371.222.21769.71318.629.71.36.776.4NaNNaNc("Mix sausage, panko, egg yolk and Wocestershire sauce in a bowl.", "Beat egg white with water in a bowl.", "Add panko and flour to separate bowls.", "Keeping hands cold will help you wrap the sausage around the hard boiled egg, I usually keep a bowl of ice water to dip my one hand inches.", "Portion out the sausage into 6 or 7 portions (same amount of eggs you have - smaller sizes work best).", "Roll egg into flour, then flatten portion of sausage into palm of hand and wrap entirely around the egg.", \n"Roll in flour again, then in egg, lastly into panko.", "Place on a jelly roll pan.", "Bake at 375 for 25 minutes.")